speech network definition

A Definition of Speech Community in Sociolinguistics

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• An Introduction to Punctuation
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• M.A., Modern English and American Literature, University of Leicester
• B.A., English, State University of New York

Speech community is a term in sociolinguistics and linguistic anthropology used to describe a group of people who share the same language,  speech  characteristics, and ways of interpreting communication. Speech communities may be large regions like an urban area with a common, distinct accent (think of Boston with its dropped r's) or small units like families and friends (think of a nickname for a sibling). They help people define themselves as individuals and community members and identify (or misidentify) others.

Speech and Identity

The concept of speech as a means of identifying with a community first emerged in 1960s academia alongside other new fields of research like ethnic and gender studies. Linguists like John Gumperz pioneered research in how personal interaction can influence ways of speaking and interpreting, while Noam Chomsky studied how people interpret language and derive meaning from what they see and hear.

Types of Communities

Speech communities can be large or small, although linguists don't agree on how they're defined. Some, like linguist Muriel Saville-Troike, argue that it's logical to assume that a shared language like English, which is spoken throughout the world, is a speech community. But she differentiates between "hard-shelled" communities, which tend to be insular and intimate, like a family or religious sect, and "soft-shelled" communities where there is a lot of interaction.

But other linguists say a common language is too vague to be considered a true speech community. The linguistic anthropologist Zdenek Salzmann describes it this way:

"[P]eople who speak the same language are not always members of the same speech community. On the one hand, speakers of South Asian English in India and Pakistan share a language with citizens of the U.S., but the respective varieties of English and the rules for speaking them are sufficiently distinct to assign the two populations to different speech communities..."

Instead, Salzman and others say, speech communities should be more narrowly defined based on characteristics such as pronunciation, grammar, vocabulary, and manner of speaking.

Study and Research

The concept of speech community plays a role in a number of social science, namely sociology, anthropology, linguists, even psychology. People who study issues of migration and ethnic identity use social community theory to study things like how immigrants assimilate into larger societies, for instance. Academics who focus on racial, ethnic, sexual​ or gender issues apply social community theory when they study issues of personal identity and politics. It also plays a role in data collection. By being aware of how communities are defined, researchers can adjust their subject pools in order to obtain representative sample populations.

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• Saville-Troike, Muriel. "The Ethnography of Communication: An Introduction, 3rd ed." Blackwell, 2003.
• Diglossia in Sociolinguistics
• Understanding Dialectology
• Descriptivism in Language
• The Difference Between a Speech and Discourse Community
• Definition and Examples of Linguistic Prestige
• The Meaning of Linguistic Imperialism and How It Can Affect Society
• Definition and Examples of Anti-Language
• What Is Dialect Prejudice?
• What Is Language Standardization?
• Language and Gender Studies
• Social Dialect or Sociolect Definition and Examples
• Discourse Domain
• Phatic Communication Definition and Examples
• Style-shifting (language)
• Definition and Examples of Rhotic and Non-Rhotic Speech
• What Is a Pidgin?

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• Published: 05 January 2022

A speech planning network for interactive language use

• Gregg A. Castellucci   ORCID: orcid.org/0000-0001-7311-2829 1 , 2 ,
• Christopher K. Kovach   ORCID: orcid.org/0000-0002-0117-151X 3 ,
• Matthew A. Howard III 3 ,
• Jeremy D. W. Greenlee   ORCID: orcid.org/0000-0002-8481-8517 3 &
• Michael A. Long   ORCID: orcid.org/0000-0002-9283-3741 1 , 2  

Nature volume  602 ,  pages 117–122 ( 2022 ) Cite this article

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During conversation, people take turns speaking by rapidly responding to their partners while simultaneously avoiding interruption 1 , 2 . Such interactions display a remarkable degree of coordination, as gaps between turns are typically about 200 milliseconds 3 —approximately the duration of an eyeblink 4 . These latencies are considerably shorter than those observed in simple word-production tasks, which indicates that speakers often plan their responses while listening to their partners 2 . Although a distributed network of brain regions has been implicated in speech planning 5 , 6 , 7 , 8 , 9 , the neural dynamics underlying the specific preparatory processes that enable rapid turn-taking are poorly understood. Here we use intracranial electrocorticography to precisely measure neural activity as participants perform interactive tasks, and we observe a functionally and anatomically distinct class of planning-related cortical dynamics. We localize these responses to a frontotemporal circuit centred on the language-critical caudal inferior frontal cortex 10 (Broca’s region) and the caudal middle frontal gyrus—a region not normally implicated in speech planning 11 , 12 , 13 . Using a series of motor tasks, we then show that this planning network is more active when preparing speech as opposed to non-linguistic actions. Finally, we delineate planning-related circuitry during natural conversation that is nearly identical to the network mapped with our interactive tasks, and we find this circuit to be most active before participant speech during unconstrained turn-taking. Therefore, we have identified a speech planning network that is central to natural language generation during social interaction.

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Neurophysiological evidence for rapid processing of verbal and gestural information in understanding communicative actions

Single-neuronal elements of speech production in humans

Data availability.

The data used in these analyses are not publicly available owing to concerns regarding patient privacy; however, the corresponding author will provide deidentified primary data upon request.

Code availability

The corresponding author will provide the MATLAB code used in this study for analysis of ECoG and behavioural data upon request.

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Acknowledgements

We thank A. Flinker, E. Jackson, J. Krivokapić, D. Schneider, N. Tritsch and members of the Long laboratory for comments on earlier versions of this manuscript; A. Ramirez-Cardenas, H. Chen, K. Ibayashi, H. Kawasaki, K. Nourski, H. Oya, A. Rhone and B. Snoad for help with data collection; and F. Guenther and N. Majaj for helpful conversations. This research was supported by R01 DC019354 (M.A.L.), R01 DC015260 (J.D.W.G.) and Simons Collaboration on the Global Brain (M.A.L.).

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Department of Neurosurgery, University of Iowa, Iowa City, IA, USA

Christopher K. Kovach, Matthew A. Howard III & Jeremy D. W. Greenlee

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Contributions

G.A.C. and M.A.L. conceived the study and designed the experiments; G.A.C., C.K.K., J.D.W.G. and M.A.L. conducted the research; G.A.C., C.K.K. and M.A.L. performed data analyses; G.A.C., C.K.K. and M.A.L. created the figures; G.A.C. and M.A.L. wrote the initial draft of the manuscript; G.A.C., C.K.K., M.A.H., J.D.W.G. and M.A.L. edited and reviewed the final manuscript. J.D.W.G. and M.A.L. acquired funding; J.D.W.G., M.A.H. and M.A.L. supervised the project.

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Correspondence to Michael A. Long .

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Extended data figures and tables

Extended data fig. 1 behaviour during the ci task..

a , Description of subprocesses assumed to occur during the perception, planning, and production windows of the CI task. b , Histograms of reaction times (RT) in early and late CI trials for all participants. c , Median RT values for early and late CI trials for all participants. d , e , Histograms depicting the distribution of average peak-to-trough response amplitudes for all electrodes displaying planning-related responses when aligned to CI onset in early and late trials ( d ) and different CI question types ( e ); median values for each distribution are indicated. Observed data (in black) are compared with a null distribution (in grey) consisting of randomly chosen timepoints ( Methods ). f , Schematics displaying GLM regressor structure for an early (top) and a late (bottom) variant of an example CI task question.

Extended Data Fig. 2 GLM temporal jittering analysis.

a , Full model R values for GLM fits of jittered high gamma activity from participant 436; each line represents data from an individual electrode. b , Example distribution of pooled D values with the fit of two Gaussians overlaid (black). The Gaussian distributions corresponding to well fit (blue) and poorly fit electrodes (red) as well as the 95 th percentile of the D distribution for poorly fit electrodes (dashed line) are indicated. D values above the 95 th percentile of the pooled distribution were deemed outliers (white bars) and not fitted. c , Table summarizing the number of electrodes rejected by the jittering analysis in each participant.  d , Table reporting the anatomical locations of electrodes rejected by the jittering analysis and electrodes displaying significant activity in the CI task. e , Scatterplot depicting the proportion of rejected electrodes within a region as a function of the proportion of responsive electrodes in a region.

Extended Data Fig. 3 Analysis of neural activity in the CI task.

a , Scatterplot depicting the distribution of all simulated task-responsive electrodes from the continuum model in three-dimensional GLM weight space; cluster membership indicated by greyscale colour. b , c , Distribution of simulated electrodes from the continuum model displaying responses in one window (i.e., unmixed) of the CI task ( b ) or multiple windows ( c ); response class indicated by colour in b and c and unmixed electrodes denoted by small black points in c . In b , simulated unmixed electrodes located outside the cluster primarily containing electrodes of the same type (i.e., ‘misclustered’) are indicated with an ‘X’. d , e , Histograms depicting the distribution of the proportion of misclustered electrodes responsive during a single task window (i.e., unmixed electrodes) ( d ), and the proportion of electrodes displaying more than one significant positive weight (i.e., mixed electrodes) ( e ) across 100,000 iterations of the continuum model simulation. The median of each distribution as well as the values observed in the actual data (dashed line) are indicated. Gold arrows indicate the bin of each distribution containing the measurements corresponding to the example iteration depicted in panels p, r, and t of Fig. 1 . f , Table reporting the number of electrodes displaying perception-related responses using either the full model or the reduced GLM lacking a planning regressor. g , h , Scatterplots depicting perception ( g ) and planning ( h ) GLM weights in the full model and reduced models lacking a planning regressor or perception regressor, respectively. Significant positive weights are denoted with filled points and nonsignificant or significant negative weights are denoted with unfilled points; the x -coordinates of each point are randomly jittered by 25% to better visualize filled versus unfilled status. No planning electrodes displayed significant perception responses in the reduced GLM lacking a planning regressor, and no perception electrodes displayed significant planning responses in the reduced GLM lacking a perception regressor.

Extended Data Fig. 4 Additional analyses of task-related activity changes.

a , Table reporting the number of perception, planning, and production-related electrodes displaying significant positive and negative weights for each GLM regressor. b , Histogram depicting mean high gamma amplitude in the first 500 ms of CI questions for all unmixed perception, planning, and production electrodes. c , d , Canonical cortical surfaces displaying electrodes with significant positive (coloured) or negative (black) GLM weights in the perception ( c ), production ( d ), and planning ( e ) windows of the CI task across all participants. Electrode diameter is scaled to the absolute magnitude of the GLM weight, and electrodes not displaying a significant weight for a given regressor are indicated with small white circles.

Extended Data Fig. 5 Anatomical analysis of responses.

a , Cortical reconstructions for all participants displaying the location of all electrodes; the size of each electrode depicts the actual size of its recording area on the cortical surface. GLM classification is indicated by electrode colour. b , Canonical cortical surfaces showing electrode locations from all participants as standard-sized white circles. c , Number of electrodes sampling each area of the canonical cortical surface (1 cm diameter spatial smoothing) after pooling electrodes from all participants. d , Proportion of electrodes displaying significant production-related responses in the CI task (1-cm-diameter spatial smoothing). e , Canonical cortical surfaces displaying electrodes with significant responses related to speech perception, production, and planning in patients with tumour (top) and patients with epilepsy (bottom) separately; electrode diameter scaled to GLM regressor weight. Electrodes not displaying a significant response for a process are depicted as small white circles.

Extended Data Fig. 6 Additional conversation-related analyses.

a , Table reporting additional turn-taking behavioural measures for each participant. b , Histograms of gap durations (time between experimenter turn offset and participant turn onset) during unconstrained conversation for each participant; bins are centred on 100 ms increments with a width of 100 ms. c , Scree plots for the PCA analysis of high gamma signals in the task (left) and conversation (right) periods of the recordings; data from each participant are represented by thin lines and the average across participants is denoted with a thick black line. The 95% confidence interval of the linear decay phase across participants ( Methods ) is also indicated. d , The observed number of electrodes whose cluster membership was not stable (i.e., switched clusters) between the task and conversation with a histogram depicting the distribution of electrode cluster switches expected by chance. e , The observed percentage of electrodes in perception, planning, and production clusters (in conversation-derived PC coefficient space) displaying significant perception, planning, and production responses (per the GLM), respectively, with histograms depicting the percentages expected by chance for each cluster type. f , Canonical cortical surfaces displaying the locations of all electrodes in perception, planning, and production clusters across participants (n = 6) in the task (left) and conversation (right). g , Table reporting summary statistics for PC activity (i.e., time-varying PC score) during unconstrained conversation for each participant.

Extended Data Fig. 7 PCA results for individual participants.

a – f , For 6 participants possessing sufficient numbers of electrodes belonging to multiple GLM classes ( Methods ): scatterplots depicting electrode distributions in PC coefficient space in the task and conversation periods (top row). Bar graphs depicting the PC coefficients for all electrodes in perception, planning, or production clusters from the PCA performed on task data and conversation data (bottom rows). Participant number given at top of each panel. g , h , For 2 participants possessing mainly planning electrodes ( Methods , Extended Data Table 1 ): bar graphs depicting the PC coefficients for all planning-related electrodes from the PCA performed on task data and conversation data. In the bar graphs, the functional categorization of PCs is indicated by filled bars coloured either green (perception), blue (planning), or red (production). Any clusters rejected due to a high proportion (50%) of mixed electrodes are indicated with grey filled bars.

Supplementary information

Reporting summary, peer review file, supplementary data 1.

List of all task stimuli.

Supplementary Data 2

All electrode locations and GLM classifications.

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Castellucci, G.A., Kovach, C.K., Howard, M.A. et al. A speech planning network for interactive language use. Nature 602 , 117–122 (2022). https://doi.org/10.1038/s41586-021-04270-z

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What is Communication Network: Examples, Types, & Importance

Table of Contents

Definition of communication network 

“Communication networks are the structures or patterns of connections among individuals or groups that facilitate the exchange of information, ideas, and resources within an organization.” – Dorothy Marcic , Richard L. Daft

What is a communication network? 

A communication network refers to an interconnected system that enables the exchange and flow of information among individuals, teams, and departments.  The communication network within an organization consists of various components such as hierarchies, departments, teams, and individuals, each with specific roles and responsibilities.

It may include both formal channels, such as official memos, emails, and hierarchical reporting lines, as well as informal channels, such as casual conversations, social networks, and grapevine communication. 

The communication network within an organization plays a crucial role in promoting information sharing, fostering teamwork, sharing organizational goals, and ensuring smooth operations across departments and teams.

Diagram of communication network 

What are the 5 types of communication networks? 

Communication networks play a crucial role in facilitating the flow of information within organizations. By understanding the different types of communication networks, organizations can optimize their internal communication and enhance collaboration among team members. Let’s explore five common types of communication networks:

1/ Wheel Network:

The Wheel Network is a communication network characterized by a central individual or hub that acts as the primary point of contact for all other members within the network. In this network, all communication channels flow through the central hub, and there are limited direct connections between other members. The central hub holds a position of authority or expertise, serving as a main point for information exchange.

Key Components and Structure:

• Central Hub : The central hub is the main individual who holds a central position within the network. They are connected to all other members and serve as the primary point for communication and coordination.
• Spoke Members : The spoke members are the individuals within the network who are connected directly to the central hub. They communicate with the hub to exchange information and may have limited direct communication with other spoke members.
• Communication Channels : The communication channels in a wheel network primarily involve the hub sharing information with the spoke members and receiving inputs or feedback from them. The spoke members typically do not communicate directly with each other.

Advantages and Disadvantages: 

Advantages:

• Centralized Communication : The central hub ensures that information flows efficiently as it is directly conveyed to all members.
• Quick Decision-making : With a central point of contact, decision-making processes can be streamlined, leading to faster responses and actions.
• Clear Chain of Command : The hierarchical structure of the wheel network provides clarity in terms of authority and reporting relationships.

Disadvantages:

• Single Point of Failure : The wheel network is highly dependent on the central hub, and if the hub is unavailable or ineffective, communication and decision-making can be drastically hampered.
• Limited Member Interaction : Direct communication between members is restricted, leading to potential information gaps and reduced collaboration.
• Overburdened Hub : The central hub may become overwhelmed with information overload, as all communication flows through them.

Examples of Wheel Network:

• CEO and Department Heads: In large organizations, the CEO often acts as the central hub, communicating with department heads who act as the spoke members. The CEO pass on information, receives updates and makes decisions based on inputs from the department heads.
• Project Manager and Team Members : In project management, the project manager serves as the hub, coordinating and communicating with team members. The project manager conveys information, sets goals, and receives updates, while team members have limited direct communication with each other.

The wheel network is suitable for situations where clear direction and control are necessary, such as in hierarchical organizations or when a central authority figure is required. However, it may not be ideal when the central hub’s absence can significantly disrupt communication .

2/ Star Network:

The Star Network refers to a communication network structure where a central individual, typically a manager or supervisor, acts as the hub for information exchange within an organization. In this network, all communication channels flow through the central hub, and other members communicate directly with the hub rather than with each other. The hub serves as a primary point of contact, coordination, and decision-making.

• Central Hub : The central hub is usually a manager, team leader, or supervisor who holds a position of authority or expertise. They serve as the primary point of communication and coordination for the team or department.
• Team Members : The team members are connected directly to the central hub. They communicate with the hub to share information, seek guidance, provide updates, and receive instructions.
• Communication Channels : Communication channels within a star network involve the hub sharing information, assigning tasks, providing feedback, and addressing inquiries or concerns raised by team members. Direct communication between team members is limited, and most communication flows through the hub.

Advantages and Disadvantages:

• Clear Reporting Structure : The star network establishes a clear reporting structure within the organization. Team members know who to communicate with, seek guidance from, and receive instructions.
• Efficient Information Exchange : Communication flows directly between team members and the hub, ensuring that information is passed on accurately and promptly.
• Limited Peer-to-Peer Interaction : Direct communication between team members is restricted in a star network, potentially limiting collaboration and problem-solving among team members.
• Dependency on the Hub : If the central hub is unavailable or inaccessible, communication within the network may be prevented, causing delays and disruptions.

Examples of Start Network:

• Department Managers: In an organization, department managers often act as central hubs within a star network. They communicate with their team members, provide guidance, allocate tasks, and ensure smooth coordination within the department.
• Executive Leadership: In larger organizations, executive leaders can serve as central hubs, sharing important company-wide announcements, communicating strategic objectives, and receiving feedback from department heads.

In organizational communication, the star network facilitates a clear chain of command, efficient information flow, and centralized control. However, Organizations should consider the nature of their communication needs and the potential trade-offs when implementing a star network structure.

3/ Vertical Network:

The Vertical Network refers to a network structure where communication channels predominantly flow vertically up and down the hierarchical levels of an organization. It emphasizes the formal chain of command and follows the reporting relationships within the organization’s structure. Information primarily flows from superiors to subordinates or from subordinates to superiors, aligning with the hierarchical structure of the organization.

Key Components and Structure :

• Higher-Level Management : This refers to the individuals occupying senior positions in the organizational hierarchy, such as executives, directors, or managers at the top level.
• Lower-Level Employees : These are the individuals positioned at lower levels of the organizational hierarchy, including employees, team members, or workers.
• Communication Channels : Communication channels in a vertical network mainly involve formal channels such as meetings, performance reviews, email exchanges, memos, and official reports. Communication flows vertically , from superiors to subordinates (downward communication) and from subordinates to superiors (upward communication).
• Clear Direction : The vertical network ensures clear communication channels and established reporting relationships, providing subordinates with clear direction and instructions from their superiors.
• Efficient Decision-making : When communication flows vertically, it allows superiors to make decisions based on information received from subordinates. This enables efficient decision-making processes aligned with organizational goals.
• Delayed Communication : Communication may take longer to reach higher levels or receive responses from superiors, as it follows the formal chain of command. This can slow down decision-making processes and responsiveness.
• Information Filtering : Communication within a vertical network may be subject to filtering or distortion as it passes through multiple levels of hierarchy. Important information may be diluted or altered, leading to miscommunication or incomplete understanding.

Examples of Vertical Networks:

• Government Bureaucracies : Government organizations and bureaucracies typically operate using a vertical network structure. Information and directives flow down the hierarchical levels, ensuring adherence to established policies and procedures. 

• Traditional Corporate Structures : Traditional hierarchical organizations, such as multinational corporations, often adopt a vertical network structure. Communication flows from executives to middle managers, who then pass it down to their respective teams and employees.

4/ Circuit Network: 

The Circuit Network refers to a network structure where communication flows through predefined paths or circuits. In this network, messages are passed sequentially from one individual or department to the next until they reach the intended recipient. The circuit network operates on the principle of fixed routes and sequential transmission of information.

• Circuit Paths : Circuit networks have predetermined paths or circuits through which information flows. Each path specifies the sequence of individuals or departments through which the message is transmitted.
• Message Routing: Messages are routed through the established circuit paths, following the predetermined order of recipients. Each recipient receives the message, processes it, and forwards it to the next recipient in the circuit.
• Sequential Transmission : Circuit networks ensure that messages are transmitted in a predetermined sequence, ensuring that each recipient receives the message in a specific order.
• Reduced Miscommunication : By following fixed paths, circuit networks can minimize the potential for miscommunication or information distortion that may occur in networks with more open communication channels.
• Control and Tracking : Circuit networks allow for better control and tracking of message flow, as each step of the circuit can be monitored and managed.
• Delayed Communication : Circuit networks may introduce delays in communication as messages need to follow a predefined path. If any recipient is unavailable or slow in forwarding the message, it can impact the overall speed of information shared.
• Lack of Flexibility: Circuit networks can be inflexible, as they follow predetermined paths. If there is a need to deviate from the established circuit, it may require additional effort or may not be possible within the network structure.

Examples of Circuit Networks:

• Approval Processes: Circuit networks are commonly used for approval processes within organizations. For instance, in a document approval process, the document passes through predetermined circuits, such as managers or department heads, for review and approval before reaching the final recipient.
• Sequential Workflows : Certain workflow processes, such as quality control in manufacturing, follow circuit networks. Each step or station in the process has a specific role and passes the product to the next step until it is completed.

5/ Chain Network:

The Chain Network refers to a linear communication structure where messages flow sequentially from one individual to the next in a chain-like fashion. In this network, communication typically starts from a sender and is passed along through a series of individuals until it reaches the final recipient. Each individual in the chain network communicates directly with only two other individuals – the one who sent the message and the one to whom the message is passed.

• Sender : The sender is the individual who initiates the cycle of communication by transmitting a message to the first recipient in the chain.
• Recipients : Recipients are the individuals who receive the message from the previous sender and pass it along to the next recipient in the chain.
• Sequential Communication : Messages flow sequentially from one recipient to the next, following a linear pattern until reaching the final recipient.
• Clear Communication Path : The chain network establishes a clear communication path, ensuring that each individual knows who they receive the message from and who they pass it to.
• Simplicity : The chain network is straightforward, with communication moving in a linear fashion, reducing complexity and potential confusion.
• Direct Feedback : The chain network allows for direct feedback as the final recipient can respond back to the sender, fostering a more immediate reception in the communication process .
• Message Distortion : As messages pass through multiple individuals in the chain, there is a higher likelihood of message distortion, especially if the message is not accurately conveyed at each step.
• Slow Transmission : Messages in a chain network may take longer to reach the final recipient, especially if there are delays in the communication flow. This can result in slower decision-making and response times.
• Lack of Flexibility : The linear nature of the chain network limits lateral communication and collaboration, as individuals typically interact only with the sender and the immediate recipient.

Examples of Chain Networks:

• Rumor Mill: Informal communication networks, often referred to as the “grapevine” or “rumor mill,” can resemble a chain network. In such networks, information spreads from one person to another sequentially, without the involvement of formal channels.

Related Reading : What is the grapevine in communication

• Message Relay : Chain networks can be seen in situations where messages need to be conveyed from one department or team to another within an organization. Each department passes the message along to the next department until it reaches the final recipient.

Types of Communication Networks in Organizations by different directions

Understanding the following communication networks and directions helps organizations establish effective channels for information exchange, collaboration, and decision-making. The most common communication direction in organizations are:

1/ Upward Communication : Upward communication flows from lower levels to higher levels of the hierarchy. Such as employees providing feedback, suggestions, reports, or seeking guidance from their superiors.

2/ Downward Communication : Downward communication flows from upper hierarchies to lower hierarchies. Such as, superiors transmit instructions, goals, policies, performance feedback, and organizational announcements to subordinates.

3/ Horizontal Communication : Also known as lateral communication , it occurs between peers or colleagues at the same hierarchical level, facilitating collaboration, coordination, and the exchange of information or ideas across different departments or teams.

4/ Diagonal Communication : Communication cuts across hierarchical levels and departments, enabling collaboration and information sharing to achieve specific goals or solve problems. The importance of diagonal communication is that It bridges gaps and enhances coordination across the organization.

Importance of a communication network in an organization

The importance of a communication network in an organization cannot be overstated. Here are some key reasons why communication networks are crucial:

1/ Conflict Resolution : Communication networks play a vital role in resolving conflicts within an organization. They provide platforms for open and constructive dialogue, allowing individuals or teams to address issues, clarify misunderstandings, and find mutually beneficial solutions. 

2/ Employee Engagement and Morale : Effective communication networks contribute to high employee engagement and morale. When employees feel informed and valued, they are more likely to be motivated and productive in their work . 

3/ Organizational Alignment : A communication network helps align individuals and departments within an organization. It ensures that everyone is aware of the organization’s objectives, strategies, and changes. This alignment promotes consistency and a shared understanding of organizational goals.

4/ Efficient Information Flow: An effectively structured communication network guarantees the seamless and efficient circulation of information across the entire organization. It enables the sharing of important messages, instructions, goals, and updates, facilitating effective coordination and decision-making.

5/ Collaboration and Teamwork: Communication networks foster collaboration and teamwork by providing channels for individuals and teams to exchange ideas and work together towards common goals. It encourages cooperation and problem-solving among employees.

Communication networks in business communication

Communication networks in business communication refer to the structures or patterns through which information is exchanged among individuals or departments within a business or organization. These networks determine how communication flows, who interacts with whom, and the channels used for sharing information. 

Communication networks help establish effective channels, and connections among employees, facilitating the exchange of ideas within the process of business communication . A well-designed communication network promotes efficient information sharing, enhances teamwork, and plays a vital role in driving the overall success and productivity of the organization.

Comparison of communication network 

In comparing different communication networks, there are both similarities and differences to consider.

Similarities:

• All communication networks aim to facilitate the exchange of information within an organization.
• They provide channels for communication, enabling individuals or departments to connect and share information.
• Communication networks help establish structure and patterns for information flow and collaboration.

Differences:

• The structure and flow of communication differ among network types. For example, the wheel network revolves around a central hub, while the chain network follows a sequential path.
• The level of direct interaction between network members varies. Some networks promote direct peer-to-peer communication (e.g., star network), while others emphasize communication through a central hub (e.g., wheel network).
• Communication networks have varying degrees of flexibility, scalability, and adaptability to different organizational needs.

Factors Influencing the Choice of Network:

Several factors influence the choice of a communication network in an organization:

1/ Organizational Structure : The hierarchical structure and reporting relationships within the organization play a role in determining the most suitable network. For example, a vertical network may align well with a highly formal organization.

Related Reading : Difference between a formal and informal organization structure

2/ Collaboration Requirements : The extent of collaboration and teamwork required within the organization influences the choice of network. Networks that promote direct interaction between members (e.g., star network) may be favored for fostering collaboration.

3/ Organizational Culture : The organization’s values, norms, and communication preferences may impact the choice of network. Some cultures may emphasize centralized communication and decision-making (e.g., wheel network), while others prioritize decentralized and inclusive communication (e.g., horizontal network).

What is the role of a communication network in information exchange 

Communication networks play a crucial role in facilitating the exchange of information within organizations. They provide a framework for people to share opinions, and important messages with one another. Here are some key roles that communication networks fulfill in information exchange:

1/ Sharing Information: Communication networks allow individuals and departments to share relevant information with each other. It enables the sharing of updates, announcements, policies, and procedures across the organization. This ensures that every individual has the necessary access to information required for optimal performance in their respective roles.

2/ Coordination and Collaboration: Communication networks facilitate coordination and collaboration among individuals and teams. They enable employees to communicate, share ideas, and work together on projects and tasks. Through these networks, employees can exchange thoughts, seek clarifications, and coordinate their efforts, fostering a sense of teamwork and synergy within the organization.

3/ Decision-Making Support: Communication networks play a crucial role in supporting decision-making processes. They allow relevant information to be conveyed to decision-makers, enabling them to gather insights, consider different perspectives, and make informed choices. By providing a platform for information exchange, networks contribute to well-informed decision-making that aligns with organizational goals.

4/ Feedback and Evaluation: Communication networks enable the exchange of feedback, both positive and constructive, among team members and between employees and managers. This feedback loop facilitates individual performance improvement and enables the identification of areas for growth. It also provides a mechanism for evaluating progress and making necessary adjustments.

Frequently Asked Questions

Q1) what is an example of a communication network.

Ans: One example of a communication network is the “Wheel Network.” In this network, communication flows through a central individual or hub that acts as the primary point of contact and coordination. All members of the network communicate with the central hub, while direct communication between members is limited. 

Q2) What are the 5 different communication networks? 

Ans: There are five different communication networks commonly observed in organizations: Wheel, Star, Vertical, Circuit, and Chain networks. Each network offers unique advantages and considerations for successful communication and collaboration within organizations.

Q3) What is a communication network and its types? 

Ans: A communication network refers to the framework through which information circulates within an organization. There are different types of communication networks, including the Wheel, Star, Vertical, Circuit, and Chain networks. Each network type has its own characteristics and implications for communication and collaboration within organizations.

Q4) Which communication network is best? 

Ans: The Star network is considered to be a commonly preferred communication network for its efficient information exchange and direct communication channels with the central hub.

Q5) What are the three communication networks? 

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How to represent speech as a network

Network of Transcript Semantics

I wrote an NLP-package for analysing the content of natural speech during my postdoc at the University of Cambridge. The algorithm ( netts ) constructs networks from a speech transcript that represent the content of what the speaker said. The idea here is that the nodes in the network show the entities that the speaker mentioned, like a cat, a house, etc. (usually nouns). And the edges of the network show the relationships between the entities. We called these networks semantic speech networks .

Why did we want to represent speech content as a network? There is evidence that psychiatric conditions, in particular early psychosis (schizophrenia), can be traced early in abnormal language. In psychosis, there is a phenomenon called ‘loosening of associations’, where connection between ideas can become tenuous and extraneuous concepts seem to intrude into the line of thought. We hypothesised that mapping speech content as a network could capture this abnormal language features early in psychosis - potentially aiding diagnosis and disease monitoring. In a clinical sample of patients with early psychosis and controls, that was indeed the case (you can find the paper here ).

We also believe that this tool could be useful for analysing speech content in other conditions and also in healthy and developing populations - the semantic speech networks are quite rich in information. If you’re interested in using semantic speech networks for your own data, we have written an installation guide . You can also use our interactive tutorials on creating and analysing semantic speech networks.

4. How does netts work?

Let’s quickly walk through how netts works. This is more extensively covered in our paper , but briefly reviewing the processing steps will help us understand the semantic speech networks.

Preprocessing

Netts first expands the most common English contractions (e.g. expanding I’m to I am ). It then removes interjections ( Mh , Uhm ). Netts also removes any transcription notes (e.g. timestamps, [inaudible] ) that were inserted by the transcriber. The user can pass a file of transcription notes that should be removed from the transcripts before processing. See Configuration for a step-by-step guide on passing custom transcription notes to netts for removal. Netts does not remove stop words or punctuation to stay as close to the original speech as possible.

Netts then uses CoreNLP to perform sentence splitting, tokenization, part of speech tagging, lemmatization, dependency parsing and co-referencing on the transcript. Netts uses the default language model implemented in CoreNLP.

We describe these Natural Language Processing steps briefly in the following. The transcript is first split into sentences (sentence splitting). It is then further split into meaningful entities, usually words (tokenization). Each word is assigned a part of speech label. The part of speech label indicates whether the word is a verb, noun, or another part of speech (part of speech tagging). Each word is also assigned their dictionary form or lemma (lemmatization). Next, the grammatical relationship between words is identified (dependency parsing). Finally, any occurrences where two or more expressions in the transcript refer to the same entity are identified (co-referencing). For example where a noun man and a pronoun he refer to the same person.

Finding nodes and edges

Netts submits each sentence to OpenIE5 for relation extraction. Openie5 extracts semantic relationships between entities from the sentence. For example, performing relation extraction on the sentence I see a man identifies the relation see between the entities I and a man . From these extracted relations, netts creates an initial list of the edges that will be present in the semantic speech network. In the edge list, the entities are the nodes and the relations are the edge labels.

Next, netts uses the part of speech tags and dependency structure to extract edges defined by adjectives or prepositions: For instance, a man on the picture contains a preposition edge where the entity a man and the picture are linked by an edge labelled on . An example of an adjective edge would be dark background . Here, dark and background are linked by an implicit is . These adjective edges and preposition edges are added to the edge list. During the next processing steps this edge list is further refined.

Refining nodes and edges

After creating the edge list, netts uses the co-referencing information to merge nodes that refer to the same entity. This is to take into account cases different words refer to the same entity. For example in the case where the pronoun he is used to refer to a man or in the case where the synonym the guy is used to refer to a man . Every entity mentioned in the text should be represented by a unique node in the semantic speech network. Therefore, nodes referring to the same entity are merged by replacing the node label in the edge list with the most representative node label (first mention of the entity that is a noun). In the example above, he and the guy would be replaced by a man . Node labels are then cleaned of superfluous words such as determiners. For example, a man would turn into man .

Constructing network

In the final step, netts constructs a semantic speech network from the edge list using networkx . The network is then plotted and saves the output. The output consists of the networkx object, the network image and the log messages from netts. The resulting network (a MultiDiGraph ) is directed and unweighted, and can have parallel edges and self-loops. Parallel edges are two or more edges that link the same two nodes in the same direction. A self-loop is an edge that links a node with itself.

If you would like to learn how to use netts to create semantic speech networks, have a look at my walk through creating and analysing semantic speech networks.

Documentation : https://alan-turing-institute.github.io/netts/

Source Code : https://github.com/alan-turing-institute/netts

Media Coverage : Medscape Article

Contributors

Netts was written by Caroline Nettekoven in collaboration with Sarah Morgan .

Netts was packaged in collaboration with Oscar Giles , Iain Stenson and Helen Duncan .

Caroline Nettekoven

Postdoctoral researcher.

I am interested in the neural basis of complex behaviour. To study this, I use neuroimaging techniques, computational modelling of behaviour and brain stimulation.

• How to create semantic speech networks
• How to analyse semantic speech networks
• Semantic speech networks linked to formal thought disorder in early psychosis
• Semantic speech networks capture formal thought disorder in psychosis

A neural network is a  machine learning program, or model, that makes decisions in a manner similar to the human brain, by using processes that mimic the way biological neurons work together to identify phenomena, weigh options and arrive at conclusions.

Every neural network consists of layers of nodes, or artificial neurons—an input layer, one or more hidden layers, and an output layer. Each node connects to others, and has its own associated weight and threshold. If the output of any individual node is above the specified threshold value, that node is activated, sending data to the next layer of the network. Otherwise, no data is passed along to the next layer of the network.

Neural networks rely on training data to learn and improve their accuracy over time. Once they are fine-tuned for accuracy, they are powerful tools in computer science and  artificial intelligence , allowing us to classify and cluster data at a high velocity. Tasks in speech recognition or image recognition can take minutes versus hours when compared to the manual identification by human experts. One of the best-known examples of a neural network is Google’s search algorithm.

Neural networks are sometimes called artificial neural networks (ANNs) or simulated neural networks (SNNs). They are a subset of machine learning, and at the heart of deep learning models.

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Think of each individual node as its own linear regression model, composed of input data, weights, a bias (or threshold), and an output. The formula would look something like this:

∑wixi + bias = w1x1 + w2x2 + w3x3 + bias

output = f(x) = 1 if ∑w1x1 + b>= 0; 0 if ∑w1x1 + b < 0

Once an input layer is determined, weights are assigned. These weights help determine the importance of any given variable, with larger ones contributing more significantly to the output compared to other inputs. All inputs are then multiplied by their respective weights and then summed. Afterward, the output is passed through an activation function, which determines the output. If that output exceeds a given threshold, it “fires” (or activates) the node, passing data to the next layer in the network. This results in the output of one node becoming in the input of the next node. This process of passing data from one layer to the next layer defines this neural network as a feedforward network.

Let’s break down what one single node might look like using binary values. We can apply this concept to a more tangible example, like whether you should go surfing (Yes: 1, No: 0). The decision to go or not to go is our predicted outcome, or y-hat. Let’s assume that there are three factors influencing your decision-making:

• Are the waves good? (Yes: 1, No: 0)
• Is the line-up empty? (Yes: 1, No: 0)
• Has there been a recent shark attack? (Yes: 0, No: 1)

Then, let’s assume the following, giving us the following inputs:

• X1 = 1, since the waves are pumping
• X2 = 0, since the crowds are out
• X3 = 1, since there hasn’t been a recent shark attack

Now, we need to assign some weights to determine importance. Larger weights signify that particular variables are of greater importance to the decision or outcome.

• W1 = 5, since large swells don’t come around often
• W2 = 2, since you’re used to the crowds
• W3 = 4, since you have a fear of sharks

Finally, we’ll also assume a threshold value of 3, which would translate to a bias value of –3. With all the various inputs, we can start to plug in values into the formula to get the desired output.

Y-hat = (1*5) + (0*2) + (1*4) – 3 = 6

If we use the activation function from the beginning of this section, we can determine that the output of this node would be 1, since 6 is greater than 0. In this instance, you would go surfing; but if we adjust the weights or the threshold, we can achieve different outcomes from the model. When we observe one decision, like in the above example, we can see how a neural network could make increasingly complex decisions depending on the output of previous decisions or layers.

In the example above, we used perceptrons to illustrate some of the mathematics at play here, but neural networks leverage sigmoid neurons, which are distinguished by having values between 0 and 1. Since neural networks behave similarly to decision trees, cascading data from one node to another, having x values between 0 and 1 will reduce the impact of any given change of a single variable on the output of any given node, and subsequently, the output of the neural network.

As we start to think about more practical use cases for neural networks, like image recognition or classification, we’ll leverage supervised learning, or labeled datasets, to train the algorithm. As we train the model, we’ll want to evaluate its accuracy using a cost (or loss) function. This is also commonly referred to as the mean squared error (MSE). In the equation below,

• i represents the index of the sample,
• y-hat is the predicted outcome,
• y is the actual value, and
• m is the number of samples.

𝐶𝑜𝑠𝑡 𝐹𝑢𝑛𝑐𝑡𝑖𝑜𝑛= 𝑀𝑆𝐸=1/2𝑚 ∑129_(𝑖=1)^𝑚▒(𝑦 ̂^((𝑖) )−𝑦^((𝑖) ) )^2

Ultimately, the goal is to minimize our cost function to ensure correctness of fit for any given observation. As the model adjusts its weights and bias, it uses the cost function and reinforcement learning to reach the point of convergence, or the local minimum. The process in which the algorithm adjusts its weights is through gradient descent, allowing the model to determine the direction to take to reduce errors (or minimize the cost function). With each training example, the parameters of the model adjust to gradually converge at the minimum.  

See this IBM Developer article for a deeper explanation of the quantitative concepts involved in neural networks .

Most deep neural networks are feedforward, meaning they flow in one direction only, from input to output. However, you can also train your model through backpropagation; that is, move in the opposite direction from output to input. Backpropagation allows us to calculate and attribute the error associated with each neuron, allowing us to adjust and fit the parameters of the model(s) appropriately.

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Neural networks can be classified into different types, which are used for different purposes. While this isn’t a comprehensive list of types, the below would be representative of the most common types of neural networks that you’ll come across for its common use cases:

The perceptron is the oldest neural network, created by Frank Rosenblatt in 1958.

Feedforward neural networks, or multi-layer perceptrons (MLPs), are what we’ve primarily been focusing on within this article. They are comprised of an input layer, a hidden layer or layers, and an output layer. While these neural networks are also commonly referred to as MLPs, it’s important to note that they are actually comprised of sigmoid neurons, not perceptrons, as most real-world problems are nonlinear. Data usually is fed into these models to train them, and they are the foundation for computer vision, natural language processing , and other neural networks.

Convolutional neural networks (CNNs) are similar to feedforward networks, but they’re usually utilized for image recognition, pattern recognition, and/or computer vision. These networks harness principles from linear algebra, particularly matrix multiplication, to identify patterns within an image.

Recurrent neural networks (RNNs) are identified by their feedback loops. These learning algorithms are primarily leveraged when using time-series data to make predictions about future outcomes, such as stock market predictions or sales forecasting.

Deep Learning and neural networks tend to be used interchangeably in conversation, which can be confusing. As a result, it’s worth noting that the “deep” in deep learning is just referring to the depth of layers in a neural network. A neural network that consists of more than three layers—which would be inclusive of the inputs and the output—can be considered a deep learning algorithm. A neural network that only has two or three layers is just a basic neural network.

To learn more about the differences between neural networks and other forms of artificial intelligence,  like machine learning, please read the blog post “ AI vs. Machine Learning vs. Deep Learning vs. Neural Networks: What’s the Difference? ”

The history of neural networks is longer than most people think. While the idea of “a machine that thinks” can be traced to the Ancient Greeks, we’ll focus on the key events that led to the evolution of thinking around neural networks, which has ebbed and flowed in popularity over the years:

1943: Warren S. McCulloch and Walter Pitts published “ A logical calculus of the ideas immanent in nervous activity  (link resides outside ibm.com)” This research sought to understand how the human brain could produce complex patterns through connected brain cells, or neurons. One of the main ideas that came out of this work was the comparison of neurons with a binary threshold to Boolean logic (i.e., 0/1 or true/false statements).   

1958: Frank Rosenblatt is credited with the development of the perceptron, documented in his research, “ The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain ” (link resides outside ibm.com). He takes McCulloch and Pitt’s work a step further by introducing weights to the equation. Leveraging an IBM 704, Rosenblatt was able to get a computer to learn how to distinguish cards marked on the left vs. cards marked on the right.

1974: While numerous researchers contributed to the idea of backpropagation, Paul Werbos was the first person in the US to note its application within neural networks within his PhD thesis  (link resides outside ibm.com).

1989: Yann LeCun published a paper (link resides outside ibm.com) illustrating how the use of constraints in backpropagation and its integration into the neural network architecture can be used to train algorithms. This research successfully leveraged a neural network to recognize hand-written zip code digits provided by the U.S. Postal Service.

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These terms are often used interchangeably, but what differences make each a unique technology?

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Difference between speech, language and communication

• 25 September 2020
• Science outreach

In our day-to-day language, the terms speech, language, and communication are often used interchangeably. However, are these words synonyms? As it turns out, no, they are not! 

Here is how to better distinguish these terms:

Speech refers to the way we produce and perceive the consonants and vowels that form all the languages in the world. It can be considered the perceptual and motor components of oral language. More specifically, it includes the following elements:

• Voice. This refers to the way we use our vocal folds (sometimes called cords), in the larynx, and our respiration (especially the expiration) to produce speech sounds. Our voice varies in intensity and pitch – that is, it can be more or less loud and have a higher or lower pitch. These parameters are determined by the contraction and extension of the vocal folds.
• Articulation. It is the way we use our articulators, including our lips and our tongue, to produce speech sounds. For example, our lips are rounded to produce the vowel /o/, while they are stretched to produce the vowel /i/.
• Resonance. This refers to the modification of the sound generated by the vocal folds as it travels through the cavities formed by the pharynx as well as the inside of our nose and mouth. Resonance influences the quality of speech sounds (a nasal vowel such as “an” vs an oral vowel such as “a”) and depends mostly on our capacity to control the amount of air that is expelled from our nose when we speak. To block air from going through the nose, we lift soft palate (also called velopharynx); to allow air going into the nose, we drop the soft palate (see figure 1). For example, too much airflow through the nose results in a nasal voice (Kummer). It should be noted that damage to resonance or to the respiratory system is likely to make speech less natural and intelligible (ASHA). 
• Fluency. This concerns the rhythm of our speech and is characterized by the number of hesitations and repetitions of sounds when we speak. Non fluent speech is associated with communication disorders such as stuttering.
• Perception. The ability to detect and perceive fine variations in the acoustic signal of speech, including variations in intensity and frequency in a locutor’s voice or variations in their speech rate, are also key elements of speech at the receptive level.

Language refers to the comprehension and production of words and sentences to share ideas or information. Language can be oral, written, or signed (e.g. Quebec Sign Language). Below are the different spheres of language (ASHA; Bishop et al, 2017): 

• Phonology. At the interface between speech and language, phonology refers to the ability to identify and use speech sounds to distinguish the words of a language. For example, in English, it is important to distinguish the sounds associated with the letters “b” and “p” since words such as “bay” and “pay” do not have the same meaning.
• Morphology. This refers to the rules that regulate the use of morphemes, the smallest units of language that carry meaning. For example, in oral and written English, the plural is often indicated by adding the morpheme “-s” to a noun (eg. anemones). Some morphemes can be added at the beginning or at the end of a word to slightly modify the meaning. For example, the morpheme ‘’-est’’ in English is used to express the superlative. For example, when we add “est” to the adjective  tall, we  create the word  tallest , meaning the person who is the most tall.
• Lexicology and semantics. These components refer to vocabulary as well as the knowledge of the word meaning (e.g., knowing the word  anemone  and that it refers not only to a marine animal, but also to a colorful perennial plant).
• Syntax. This refers to the rules to combine words to create sentences in a language. For example, the sentence ‘’I love anemones’’ is composed of a subject (I) and a predicate (formed by the verb  love  and the noun  anemones ); the two obligatory components in an English sentence.
• Pragmatics. This refers to the rules about the use of language in a specific communication context. These rules include the respect of the turn-taking or the adjustment of the language level or content based on the interlocutor. It also includes the ability to detect humour, irony and sarcasm.

Communication

Communication refers to the process of exchanging information, including emotions and thoughts (Bishop and al., 2016), with others using speaking, writing, signs, facial expressions and body language. Communication thus incorporates speech and language, but also prosody (linguistic and emotional). Prosody refers to the ability to vary the intonation, rate and voice intensity to either emphasize certain syllables or words when we speak or to draw the attention of our interlocutor to a particular piece of information (linguistic prosody), or to convey our emotions, voluntarily or not (emotional prosody; Wilson & Wharton, 2005). 

Although the words  speech ,  language  and  communication  are often used interchangeably, these words have distinct meanings when used in scientific or clinical contexts. While  communication  is a broad concept,  speech  and  language  have very specific meaning. This is important because communication difficulties can affect speech and language independently. For example, a person with a speech impairment may have difficulty articulating correctly without having any language difficulty. Likewise, a person with a language disorder may have difficulty understanding the meaning of words, forming grammatically sentences, respecting speaking turns during a conversation, etc., while having no difficulty related to speech (normal voice, normal articulation).

Suggested readings:

• The cocktail party explained
• Comic strip about speech
• Speech perception: a complex ability
• What is the most important element of communication?

Speech analysis

• What is prosody?

References:

American Speech and Hearing Association (ASHA). (2020, September 1 rst ). What Is Speech? What Is Language? https://www.asha.org/public/speech/development/speech-and-language/

American Speech and Hearing Association (ASHA). (2020, September 1 rst ). Language in brief. https://www.asha.org/Practice-Portal/Clinical-Topics/Spoken-Language-Disorders/Language-In–Brief/

American Speech and Hearing Association (ASHA). (2020, September 23). Dysarthria in Adults. https://www.asha.org/PRPSpecificTopic.aspx?folderid=8589943481&section=Signs_and_Symptoms

Bishop, D.V.M., Snowling, M.J., Thompson, P.A., Greenhalgh, T., & CATALISE consortium. (2016). CATALISE: A Multinational and Multidisciplinary Delphi Consensus Study. Identifying Language Impairments in Children. PLOS ONE 11 (12): e0168066.  https://doi.org/10.1371/journal.pone.0168066

Kummer, A.W. (2020, September 23). Resonance Disorders and Velopharyngeal Dysfunction.  https://www.cincinnatichildrens.org/- /media/cincinnati%20childrens/home/service/s/speech/patients/handouts/resonance-disorders-and-vpd.pdf?la=en

Wilson, D., & Wharton, T. (2006). Relevance and prosody. Journal of Pragmatics 38 , 1559–1579. doi:10.1016/j.pragma.2005.04.012

The peripheral auditory system

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Definition of networking

Examples of networking in a sentence.

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1967, in the meaning defined at sense 1

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• social networking

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What Is Speech Analytics?

Speech analytics records and analyzes conversations to understand the content and sentiment of business communication.

Speech analytics is a combination of technologies that capture and analyze spoken language to extract business insights. It focuses on what is said and how it’s said, offering a comprehensive understanding of both the content and context of the conversations.

In today’s business landscape, speech analytics enables organizations to improve customer service , minimize costs, ensure compliance and gain a competitive edge by better understanding customer needs and sentiments.

As businesses increasingly adopt digital communication channels, speech analytics is evolving to provide real-time insights across different ways consumers interact with a business.

What Industries Use Speech Analytics?

Speech analytics traditionally occurs in contact centers, where customer interactions are recorded and analyzed to improve service quality. Recently, though, it’s expanding to industries such as sales, marketing and even internal communications.

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How Does Speech Analytics Work? 

Core technologies.

The core technologies behind speech analytics include the following.

• Automatic speech recognition : Converts spoken language into text.
• Natural language processing : Analyzes and interprets the meaning and context of the transcribed text.
• Machine learning algorithms : Identify patterns, trends and anomalies in the data they collect.

Speech analytics typically follow these steps.

• Audio capture : Recording of conversations through various channels such as phone calls, video calls and face-to-face interactions.
• Transcription : Converting the audio recordings into text using automatic speech recognition.
• Embedding acoustic data : Pairing the acoustic data with the timings from the recognized transcript to capture how something is said.
• Text analysis : Applying NLP to understand the sentiment, keywords and context.
• Data analysis and visualization : Analyzing the combined data and presenting it in a user-friendly format, often through dashboards and reports, to derive business insights.

Features of Speech Analytics

The core feature of any speech analytics system is the speech-to-text engine or recognizer, which is responsible for converting spoken language into a text transcript.

This transcript is the foundation for building further analysis, allowing natural language processing and machine learning algorithms to process the content and context of the conversation.

Speech analytics is essentially text analytics applied to the recognized text of a conversation. It goes beyond mere text analysis, though, by incorporating conversation data such as pace, tone and volume, adding layers of context that provide a richer understanding of the interaction.

The outputs of speech analytics are multi-faceted and highly valuable to businesses, including the following.

• Transcripts : Accurate renderings of the spoken conversation into text form, which can be stored, searched and analyzed.
• Call scores : Evaluations based on targeted sentiments such as satisfaction, experience, participation, engagement, responsiveness, complexity and effort.
• Notable events : Key moments in the conversation, such as agreement, dissatisfaction or escalation, that require attention or action.
• Reasons for interaction : Insights into why the conversation took place, identifying the underlying motivations or issues.
• Topics and segments : Identification of the main topics discussed and the segmentation of the conversation into relevant parts for more focused analysis.

These features collectively allow businesses to extract actionable insights from spoken interactions, driving improvements in customer service, compliance and overall operational efficiency.

Speech Analytics Tools

In the rapidly evolving field of speech analytics, we can categorize tools based on their primary focus and capabilities. Here are some key categories.

Integration-Focused Tools

These tools emphasize seamless integration with existing business systems, such as customer relationship management platforms , enterprise resource planning systems and contact center infrastructure. They are designed to plug into existing workflows with minimal disruption, allowing businesses to quickly implement speech analytics without overhauling their systems.

Real-Time Analytics Tools

Real-time analytics tools offer immediate insights during customer interactions, enabling on-the-fly adjustments in response to customer sentiment, tone and context. These tools are critical in contact centers and sales organizations where instant feedback can directly influence outcomes.

Feature-Rich Tools

Some tools are distinguished by their robust feature sets, which may include the following.

• Query engine : Allows users to perform complex searches across large conversational data sets to uncover specific patterns or insights.
• Quality assurance : Ensures that interactions meet predefined standards by monitoring and scoring calls based on key performance indicators.
• Data streams : Facilitates the real-time or near-real-time processing of voice data, ensuring that insights are as current as possible.
• Applications and plug-ins : These tools often support various applications or plug-ins, allowing businesses to customize their speech analytics environment to meet specific needs, such as sentiment analysis , keyword spotting or automated call summaries.

Applications of Speech Analytics 

Industries using speech analytics tools.

Speech analytics tools are critical in industries with frequent customer interaction.

• Contact centers : These are the primary users of speech analytics, using it to enhance customer service, monitor agent performance and ensure compliance.
• Sales organizations : Speech analytics helps in analyzing sales calls to identify successful strategies and improve overall sales effectiveness.
• Healthcare : In healthcare, speech analytics can be used to improve patient interactions, monitor compliance with regulations and provide better training for patient-facing staff.
• Financial services : Financial institutions use these tools for compliance monitoring, fraud detection and improving customer service.

Real-Time and Post-Call Analytics

• Real-time analytics : Provides immediate insights during a call, allowing agents to adjust their approach based on customer sentiment and context.
• Post-call analytics : Analyzes recorded calls to identify trends, measure performance and develop strategies for improvement.

Benefits of Using Speech Analytics 

Improving customer service.

Speech analytics helps in understanding customer needs and sentiments, enabling organizations to provide personalized service. It can identify frequent issues and provide insights into customer expectations, leading to improved customer satisfaction.

By analyzing the tone and emotion in customer interactions, businesses can also address problems more proactively, leading to a better overall customer experience.

Saving Resources

By automating the analysis of customer interactions, speech analytics reduces the need for manual call monitoring, saving time and resources. It helps identify areas where you can optimize processes, leading to cost savings.

Additionally, speech analytics can pinpoint areas where you can improve self-service options, further reducing the workload on customer service representatives and lowering operational costs.

Enhancing Compliance and Risk Management

Speech analytics ensures compliance with industry regulations by monitoring all interactions for specific keywords and phrases. It can alert management to potential compliance breaches, reducing the risk of fines and legal issues. This proactive monitoring helps maintain high standards of compliance and minimizes the risk of non-compliance penalties.

Retaining Talent and Training Agents Faster

Speech analytics can be instrumental in employee training and development. By analyzing interactions, managers can identify areas where agents need improvement and provide targeted coaching.

This leads to faster training times and helps in retaining talent by continuously developing their skills and enhancing their job satisfaction. Effective training and coaching also lead to higher performance levels and reduced turnover rates.

Process Optimization and Self-Service Improvement

Identifying areas for self-service improvement is another significant benefit. Speech analytics can reveal common customer inquiries and issues that could be resolved through automated systems. By enhancing self-service options, businesses can reduce the volume of calls handled by human agents, allowing them to focus on more complex and value-added interactions.

Enhancing Customer Insights

Speech analytics provides insights into customer preferences, behavior and emerging trends. You can use this data to tailor products, services and marketing strategies to better meet customer needs. Understanding customer sentiment and feedback helps in making informed business decisions and improving overall customer engagement.

Improving Agent Performance and Customer Interactions

By providing real-time feedback to agents during calls, speech analytics can improve the quality of customer interactions. It can alert agents to use certain phrases or avoid specific words, leading to more positive customer experiences. Continuous monitoring and feedback help agents refine their communication skills and handle interactions more effectively.

Reducing Churn and Increasing Customer Retention

Speech analytics can identify patterns and warning signs of customer dissatisfaction, enabling businesses to take proactive measures to address issues before they lead to churn . By understanding and addressing the root causes of dissatisfaction, businesses can improve customer retention rates and build long-term loyalty .

Enhancing Strategic Planning

The insights gained from speech analytics can inform strategic planning and decision-making processes. By understanding customer needs and market trends, businesses can develop better strategies for growth and competitive advantage. Speech analytics provides a wealth of data that businesses can use to guide product development, marketing and customer service initiatives.

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Limitations of Using Speech Analytics 

Technical challenges.

Implementing speech analytics can be technically challenging, requiring robust infrastructure and integration with existing systems. Ensuring the accuracy of transcriptions and interpretations, especially with diverse accents and languages , can also be difficult.

Privacy Concerns

The use of speech analytics raises privacy concerns, as it involves recording and analyzing customer interactions. Organizations must comply with data protection regulations and obtain necessary consent from customers. This is particularly important in industries where recording conversations is not a standard practice.

For instance, general contact centers that handle routine inquiries or sales transactions typically require a notification at the beginning of a call, informing the customer that calls may be monitored for quality assurance or similar purposes. This notice is a legal requirement in many jurisdictions to ensure transparency and obtain implicit consent for recording.

In contrast, certain types of contact centers, such as emergency services (e.g., 911 centers), may not require explicit consent to record interactions. These centers operate under different regulatory frameworks where the primary focus is on public safety, and recording is deemed essential for operational effectiveness.

Even in these cases, though, organizations must still adhere to relevant privacy laws and securely store recordings, accessed only by authorized personnel.

Frequently Asked Questions

What’s the role of a speech analyst.

A speech analyst is a specialist responsible for interpreting the data generated by speech analytics tools. Their primary tasks include analyzing call recordings, identifying trends and providing actionable insights to help businesses improve customer service, ensure compliance and optimize operational efficiency.

How does speech analytics differ from text analytics?

While text analytics focuses on analyzing written content, speech analytics starts with spoken language, converting it to text and then applying similar analytical techniques. Speech analytics adds a layer of complexity by considering factors like tone, pace and volume.

What are challenges in implementing speech analytics?

Implementing speech analytics can be challenging due to technical requirements and the need for integration with existing systems. For real-time analytics, integration with computer-telephony integration systems is crucial, allowing data capture and analysis during live interactions. Ensuring that speech analytics tools work smoothly within the same environment as telephony and CRM systems can be complex and require additional customization.

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| | | | | | | My Wordlists Legacy activities       Wordsmyth     Lookup History a system that involves a number of persons or groups. , , , a group of radio or television stations, or a company that controls such a group. , any system of roads or lines connected to each other like a net. , , a system of computers that are connected to one or more other computers. : a system of computers that are connected to one or more other computers.', '', '');"> ,   networks, networking, networked to create a network in or for. , See   , Subscribe for ad-free Wordsmyth and more Tips & Tricks Website & Apps ChatGPT Blogs ChatGPT News ChatGPT Tutorial What is Speech Recognition? Speech recognition or speech-to-text recognition , is the capacity of a machine or program to recognize spoken words and transform them into text. Speech Recognition  is an important feature in several applications used such as home automation, artificial intelligence, etc. In this article, we are going to explore how speech recognition software work, speech recognition algorithms, and the role of NLP. See examples of how this technology is used in everyday life and various industries, making interactions with devices smarter and more intuitive. Speech Recognition , also known as automatic speech recognition ( ASR ), computer speech recognition, or speech-to-text, focuses on enabling computers to understand and interpret human speech. Speech recognition involves converting spoken language into text or executing commands based on the recognized words. This technology relies on sophisticated algorithms and machine learning models to process and understand human speech in real-time , despite the variations in accents , pitch , speed , and slang . Key Features of Speech Recognition Accuracy and Speed: They can process speech in real-time or near real-time, providing quick responses to user inputs. Natural Language Understanding (NLU): NLU enables systems to handle complex commands and queries , making technology more intuitive and user-friendly . Multi-Language Support: Support for multiple languages and dialects , allowing users from different linguistic backgrounds to interact with technology in their native language. Background Noise Handling: This feature is crucial for voice-activated systems used in public or outdoor settings. Speech Recognition Algorithms Speech recognition technology relies on complex algorithms to translate spoken language into text or commands that computers can understand and act upon. Here are the algorithms and approaches used in speech recognition: 1. Hidden Markov Models (HMM) Hidden Markov Models have been the backbone of speech recognition for many years. They model speech as a sequence of states, with each state representing a phoneme (basic unit of sound) or group of phonemes. HMMs are used to estimate the probability of a given sequence of sounds, making it possible to determine the most likely words spoken. Usage : Although newer methods have surpassed HMM in performance, it remains a fundamental concept in speech recognition, often used in combination with other techniques. 2. Natural language processing (NLP) NLP is the area of  artificial intelligence  which focuses on the interaction between humans and machines through language through speech and text. Many mobile devices incorporate speech recognition into their systems to conduct voice search. Example such as : Siri or provide more accessibility around texting.  3. Deep Neural Networks (DNN) DNNs have improved speech recognition’s accuracy a lot. These networks can learn hierarchical representations of data, making them particularly effective at modeling complex patterns like those found in human speech. DNNs are used both for acoustic modeling , to better understand the sound of speech , and for language modeling, to predict the likelihood of certain word sequences. 4. End-to-End Deep Learning Now, the trend has shifted towards end-to-end deep learning models , which can directly map speech inputs to text outputs without the need for intermediate phonetic representations. These models, often based on advanced RNNs , Transformers, or Attention Mechanisms , can learn more complex patterns and dependencies in the speech signal. What is Automatic Speech Recognition? Automatic Speech Recognition (ASR) is a technology that enables computers to understand and transcribe spoken language into text. It works by analyzing audio input, such as spoken words, and converting them into written text , typically in real-time. ASR systems use algorithms and machine learning techniques to recognize and interpret speech patterns , phonemes, and language models to accurately transcribe spoken words. This technology is widely used in various applications, including virtual assistants , voice-controlled devices , dictation software , customer service automation , and language translation services . What is Dragon speech recognition software? Dragon speech recognition software is a program developed by Nuance Communications that allows users to dictate text and control their computer using voice commands. It transcribes spoken words into written text in real-time , enabling hands-free operation of computers and devices. Dragon software is widely used for various purposes, including dictating documents , composing emails , navigating the web , and controlling applications . It also features advanced capabilities such as voice commands for editing and formatting text , as well as custom vocabulary and voice profiles for improved accuracy and personalization. What is a normal speech recognition threshold? The normal speech recognition threshold refers to the level of sound, typically measured in decibels (dB) , at which a person can accurately recognize speech. In quiet environments, this threshold is typically around 0 to 10 dB for individuals with normal hearing. However, in noisy environments or for individuals with hearing impairments , the threshold may be higher, meaning they require a louder volume to accurately recognize speech . Uses of Speech Recognition Virtual Assistants: These are like digital helpers that understand what you say. They can do things like set reminders, search the internet, and control smart home devices, all without you having to touch anything. Examples include Siri , Alexa , and Google Assistant . Accessibility Tools: Speech recognition makes technology easier to use for people with disabilities. Features like voice control on phones and computers help them interact with devices more easily. There are also special apps for people with disabilities. Automotive Systems: In cars, you can use your voice to control things like navigation and music. This helps drivers stay focused and safe on the road. Examples include voice-activated navigation systems in cars. Healthcare: Doctors use speech recognition to quickly write down notes about patients, so they have more time to spend with them. There are also voice-controlled bots that help with patient care. For example, doctors use dictation tools to write down patient information quickly. Customer Service: Speech recognition is used to direct customer calls to the right place or provide automated help. This makes things run smoother and keeps customers happy. Examples include call centers that you can talk to and customer service bots . Education and E-Learning: Speech recognition helps people learn languages by giving them feedback on their pronunciation. It also transcribes lectures, making them easier to understand. Examples include language learning apps and lecture transcribing services. Security and Authentication: Voice recognition, combined with biometrics , keeps things secure by making sure it’s really you accessing your stuff. This is used in banking and for secure facilities. For example, some banks use your voice to make sure it’s really you logging in. Entertainment and Media: Voice recognition helps you find stuff to watch or listen to by just talking. This makes it easier to use things like TV and music services . There are also games you can play using just your voice. Speech recognition is a powerful technology that lets computers understand and process human speech. It’s used everywhere, from asking your smartphone for directions to controlling your smart home devices with just your voice. This tech makes life easier by helping with tasks without needing to type or press buttons, making gadgets like virtual assistants more helpful. It’s also super important for making tech accessible to everyone, including those who might have a hard time using keyboards or screens. As we keep finding new ways to use speech recognition, it’s becoming a big part of our daily tech life, showing just how much we can do when we talk to our devices. What is Speech Recognition?- FAQs What are examples of speech recognition. Note Taking/Writing: An example of speech recognition technology in use is speech-to-text platforms such as Speechmatics or Google’s speech-to-text engine. In addition, many voice assistants offer speech-to-text translation. Is speech recognition secure? Security concerns related to speech recognition primarily involve the privacy and protection of audio data collected and processed by speech recognition systems. Ensuring secure data transmission, storage, and processing is essential to address these concerns. Is speech recognition and voice recognition same? No, speech recognition and voice recognition are different. Speech recognition converts spoken words into text using NLP, focusing on the content of speech. Voice recognition, however, identifies the speaker based on vocal characteristics, emphasizing security and personalization without interpreting the speech’s content. What is speech recognition in AI? Speech recognition is the process of converting sound signals to text transcriptions. Steps involved in conversion of a sound wave to text transcription in a speech recognition system are: Recording: Audio is recorded using a voice recorder. Sampling: Continuous audio wave is converted to discrete values. What are the type of Speech Recognition? Dictation Systems: Convert speech to text. Voice Command Systems: Execute spoken commands. Speaker-Dependent Systems: Trained for specific users. Speaker-Independent Systems: Work for any user. Continuous Speech Recognition: Allows natural, flowing speech. Discrete Speech Recognition: Requires pauses between words. NLP-Integrated Systems: Understand context and meaning How accurate is speech recognition technology? The accuracy of speech recognition technology can vary depending on factors such as the quality of audio input , language complexity , and the specific application or system being used. Advances in machine learning and deep learning have improved accuracy significantly in recent years. Please Login to comment... Similar reads. Computer Networks Computer Subject tech-updates How to Delete Discord Servers: Step by Step Guide Google increases YouTube Premium price in India: Check our the latest plans California Lawmakers Pass Bill to Limit AI Replicas Best 10 IPTV Service Providers in Germany 15 Most Important Aptitude Topics For Placements [2024] Improve your Coding Skills with Practice What kind of Experience do you want to share? ABBREVIATIONS BIOGRAPHIES CALCULATORS CONVERSIONS DEFINITIONS   Vocabulary       What does speech mean? Definitions for speech spitʃ speech, this dictionary definitions page includes all the possible meanings, example usage and translations of the word speech ., princeton's wordnet rate this definition: 3.8 / 4 votes. address, speech noun the act of delivering a formal spoken communication to an audience "he listened to an address on minor Roman poets" speech, speech communication, spoken communication, spoken language, language, voice communication, oral communication noun (language) communication by word of mouth "his speech was garbled"; "he uttered harsh language"; "he recorded the spoken language of the streets" speech noun something spoken "he could hear them uttering merry speeches" the exchange of spoken words "they were perfectly comfortable together without speech" manner of speaking, speech, delivery noun your characteristic style or manner of expressing yourself orally "his manner of speaking was quite abrupt"; "her speech was barren of southernisms"; "I detected a slight accent in his speech" lecture, speech, talking to noun a lengthy rebuke "a good lecture was my father's idea of discipline"; "the teacher gave him a talking to" actor's line, speech, words noun words making up the dialogue of a play "the actor forgot his speech" language, speech noun the mental faculty or power of vocal communication "language sets homo sapiens apart from all other animals" Wiktionary Rate this definition: 0.0 / 0 votes The faculty of speech; the ability to speak or to use vocalizations to communicate. A session of speaking; a long oral message given publicly usually by one person. The candidate made some ambitious promises in his campaign speech. Etymology: From speche, from spæc, spræc, from sprēkijō, from spereg-. Cognate with spraak, Sprache, sprog. More at speak. Samuel Johnson's Dictionary Rate this definition: 0.0 / 0 votes Speech noun Etymology: from speak. There is none comparable to the variety of instructive expressions by speech, wherewith a man alone is endowed, for the communication of his thoughts. William Holder , on Speech. Though our ideas are first acquired by various sensations and reflections, yet we convey them to each other by the means of certain sounds, or written marks, which we call words; and a great part of our knowledge is both obtained and communicated by these means, which are called speech. Isaac Watts. In speech be eight parts. Accidence. The acts of God to human ears Cannot without process of speech be told. John Milton. There is neither speech nor language, but their voices are heard among them. Ps. Common Prayer. A plague upon your epileptick visage! Smile you my speeches as I were a fool. William Shakespeare , K. Lear. The duke did of me demand What was the speech among the Londoners, Concerning the French journey. William Shakespeare. Speech of a man’s self ought to be seldom. Francis Bacon , Essays. The constant design of these orators, in all their speeches, was to drive some one particular point. Jonathan Swift. I, with leave of speech implor’d, reply’d. John Milton. Wikipedia Rate this definition: 0.0 / 0 votes Speech is a human vocal communication using language. Each language uses phonetic combinations of vowel and consonant sounds that form the sound of its words (that is, all English words sound different from all French words, even if they are the same word, e.g., "role" or "hotel"), and using those words in their semantic character as words in the lexicon of a language according to the syntactic constraints that govern lexical words' function in a sentence. In speaking, speakers perform many different intentional speech acts, e.g., informing, declaring, asking, persuading, directing, and can use enunciation, intonation, degrees of loudness, tempo, and other non-representational or paralinguistic aspects of vocalization to convey meaning. In their speech, speakers also unintentionally communicate many aspects of their social position such as sex, age, place of origin (through accent), physical states (alertness and sleepiness, vigor or weakness, health or illness), psychological states (emotions or moods), physico-psychological states (sobriety or drunkenness, normal consciousness and trance states), education or experience, and the like. Although people ordinarily use speech in dealing with other persons (or animals), when people swear they do not always mean to communicate anything to anyone, and sometimes in expressing urgent emotions or desires they use speech as a quasi-magical cause, as when they encourage a player in a game to do or warn them not to do something. There are also many situations in which people engage in solitary speech. People talk to themselves sometimes in acts that are a development of what some psychologists (e.g., Lev Vygotsky) have maintained is the use of silent speech in an interior monologue to vivify and organize cognition, sometimes in the momentary adoption of a dual persona as self addressing self as though addressing another person. Solo speech can be used to memorize or to test one's memorization of things, and in prayer or in meditation (e.g., the use of a mantra). Researchers study many different aspects of speech: speech production and speech perception of the sounds used in a language, speech repetition, speech errors, the ability to map heard spoken words onto the vocalizations needed to recreate them, which plays a key role in children's enlargement of their vocabulary, and what different areas of the human brain, such as Broca's area and Wernicke's area, underlie speech. Speech is the subject of study for linguistics, cognitive science, communication studies, psychology, computer science, speech pathology, otolaryngology, and acoustics. Speech compares with written language, which may differ in its vocabulary, syntax, and phonetics from the spoken language, a situation called diglossia. The evolutionary origins of speech are unknown and subject to much debate and speculation. While animals also communicate using vocalizations, and trained apes such as Washoe and Kanzi can use simple sign language, no animals' vocalizations are articulated phonemically and syntactically, and do not constitute speech. ChatGPT Rate this definition: 0.0 / 0 votes Speech is a form of human communication using spoken language. It involves the vocal production of sounds, usually in a structured and conventional way, to convey a particular meaning or message. This process includes articulation, voice production, fluency, and language skills involving vocabulary, grammar, and syntax. Webster Dictionary Rate this definition: 3.0 / 1 vote the faculty of uttering articulate sounds or words; the faculty of expressing thoughts by words or articulate sounds; the power of speaking he act of speaking; that which is spoken; words, as expressing ideas; language; conversation a particular language, as distinct from others; a tongue; a dialect talk; mention; common saying formal discourse in public; oration; harangue ny declaration of thoughts to make a speech; to harangue Wikidata Rate this definition: 5.0 / 1 vote Speech is the vocalized form of human communication. It is based upon the syntactic combination of lexicals and names that are drawn from very large vocabularies. Each spoken word is created out of the phonetic combination of a limited set of vowel and consonant speech sound units. These vocabularies, the syntax which structures them, and their set of speech sound units differ, creating the existence of many thousands of different types of mutually unintelligible human languages. Most human speakers are able to communicate in two or more of them. The vocal abilities that enable humans to produce speech also provide humans with the ability to sing. A gestural form of human communication exists for the deaf in the form of sign language. Speech in some cultures has become the basis of a written language, often one that differs in its vocabulary, syntax and phonetics from its associated spoken one, a situation called diglossia. Speech in addition to its use in communication, it is suggested by some psychologists such as Vygotsky is internally used by mental processes to enhance and organize cognition in the form of an interior monologue. Chambers 20th Century Dictionary Rate this definition: 5.0 / 1 vote spēch, n. that which is spoken: language: the power of speaking: manner of speech, oration: any declaration of thoughts: mention: colloquy: conference.— ns. Speech′-craft , the science of language: the gift of speech; Speech′-crī′er , one who hawked the broadsides containing the dying speeches of persons executed, once common; Speech′-day , the public day at the close of a school year.— adj. Speech′ful , loquacious.— ns. Speechificā′tion , the act of making harangues; Speech′ifīer .— v.i. Speech′ify , to make speeches, harangue (implying contempt).— adj. Speech′less , destitute or deprived of the power of speech.— adv. Speech′lessly .— ns. Speech′lessness ; Speech′-māk′er , one accustomed to speak in public; Speech′-māk′ing , a formal speaking before an assembly; Speech′-read′ing , the art of following spoken words by observing the speaker's lips, as taught to deaf-mutes. [A.S. spǽc , sprǽc ; Ger. sprache .] U.S. National Library of Medicine Rate this definition: 0.0 / 0 votes Communication through a system of conventional vocal symbols. Editors Contribution Rate this definition: 0.0 / 0 votes The faculty or act of speaking. His speech and language developed at such a fast pace to his peers. Submitted by MaryC on January 12, 2020   To express how we feel using words and language. They decided a wedding speech was not necessary as they chose for everyone to come to the wedding and enjoy themselves, have fun, feel the love and unity and dance the night away. Submitted by MaryC on April 9, 2020   Suggested Resources Rate this definition: 0.0 / 0 votes Song lyrics by speech -- Explore a large variety of song lyrics performed by speech on the Lyrics.com website. Surnames Frequency by Census Records Rate this definition: 0.0 / 0 votes According to the U.S. Census Bureau, Speech is ranked #91625 in terms of the most common surnames in America. The Speech surname appeared 201 times in the 2010 census and if you were to sample 100,000 people in the United States, approximately 0 would have the surname Speech . 85.5% or 172 total occurrences were Black . 8.4% or 17 total occurrences were White . 3.9% or 8 total occurrences were of Hispanic origin. Matched Categories Auditory Communication Expressive Style British National Corpus Spoken Corpus Frequency Rank popularity for the word 'speech' in Spoken Corpus Frequency: #1308 Written Corpus Frequency Rank popularity for the word 'speech' in Written Corpus Frequency: #2059 Nouns Frequency Rank popularity for the word 'speech' in Nouns Frequency: #532 Usage in printed sources From:   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Anagrams for speech » How to pronounce speech. Alex US English David US English Mark US English Daniel British Libby British Mia British Karen Australian Hayley Australian Natasha Australian Veena Indian Priya Indian Neerja Indian Zira US English Oliver British Wendy British Fred US English Tessa South African How to say speech in sign language? Chaldean Numerology The numerical value of speech in Chaldean Numerology is: 2 Pythagorean Numerology The numerical value of speech in Pythagorean Numerology is: 2 Examples of speech in a Sentence Kierkegaard : People demand freedom of speech as a compensation for the freedom of thought which they seldom use. Republican governors across America are leading the charge in defending liberty and securing unmatched economic prosperity in our states, the Biden administration is governing from the far-left, ignoring the problems of working-class Americans while pushing an agenda that stifles free speech , free thought, and economic freedom. The American people have had enough, but there is an alternative and that's what I look forward to sharing on Tuesday evening. Julia Louis-Dreyfus : These last few nights have been going so well weve decided to add a fifth night where we will just play Michelle Obamas speech on a loop. Jacob Frey : So while our thriving city is open to everyone, I will continue to stand alongside people in Minneapolis and reject speech and behavior that make any of our residents less safe. Katie Sanders : I am using my right of free speech to voice the opinion that is not being heard, i expected that a petition or something of the sort would go around. I am not upset that it is happening because they have that right, but I would be upset if I were to lose my job becauseI love it and have not had any problems with any residents before this. The death threats I have been receiving are not okay and that should be universally accepted. As for the WCU Faculty Senate, the point is being proven about opposing views because they are saying our opinion is nonsense and proceeded to create t-shirts for sale. After being called out for their post, they immediately deleted it. Popularity rank by frequency of use ^  Princeton's WordNet http://wordnetweb.princeton.edu/perl/webwn?s=speech ^  Wiktionary https://en.wiktionary.org/wiki/Speech ^  Samuel Johnson's Dictionary https://johnsonsdictionaryonline.com/views/search.php?term=speech ^  Wikipedia https://en.wikipedia.org/wiki/Speech ^  ChatGPT https://chat.openai.com ^  Webster Dictionary https://www.merriam-webster.com/dictionary/speech ^  Wikidata https://www.wikidata.org/w/index.php?search=speech ^  Chambers 20th Century Dictionary https://www.gutenberg.org/files/37683/37683-h/37683-h.htm#:~:text=speech ^  Surnames Frequency by Census Records https://www.census.gov/topics/population/genealogy/data/2010_surnames.html ^  Usage in printed sources https://books.google.com/ngrams/graph?content=speech Word of the Day Would you like us to send you a free new word definition delivered to your inbox daily. Please enter your email address: Citation Use the citation below to add this definition to your bibliography:. Style: MLA Chicago APA "speech." Definitions.net. STANDS4 LLC, 2024. Web. 2 Sep. 2024. < https://www.definitions.net/definition/speech >. Discuss these speech definitions with the community: Report Comment We're doing our best to make sure our content is useful, accurate and safe. If by any chance you spot an inappropriate comment while navigating through our website please use this form to let us know, and we'll take care of it shortly. You need to be logged in to favorite . Create a new account. Your name: * Required Your email address: * Required Pick a user name: * Required Username: * Required Password: * Required Forgot your password?    Retrieve it Are we missing a good definition for speech ? Don't keep it to yourself... Image credit, the web's largest resource for, definitions & translations, a member of the stands4 network, image or illustration of. Free, no signup required : Add to chrome, add to firefox, browse definitions.net, are you a words master, the act of carrying something, Nearby & related entries:. speculum noun spedding, james speech acoustics speech act noun speech articulation tests speech assessment methods phonetic alphabet speech balloon Alternative searches for speech : Search for speech on Amazon 479 IMAGES Free photo: Communicate Definition Closeup Showing Dialog How to represent speech as a network Deep Speech: Accurate Speech Recognition with GPU-Accelerated Deep Learning The Motivation for Deep Neural Network Speech Enhancement Speech synthesis technologies will drive the next wave of innovative voice applications The Speech Network VIDEO What Is A Network? #networking IMC IMC NETWORK MARKETING MOTIVATIONAL Lecture 7 What Is SPN? Mastering Negotiation Secrets with Expert Guidance! check the description motivational speech Network marketing COMMENTS Social network (sociolinguistics) In the field of sociolinguistics, social network describes the structure of a particular speech community.Social networks are composed of a "web of ties" (Lesley Milroy) between individuals, and the structure of a network will vary depending on the types of connections it is composed of.Social network theory (as used by sociolinguists) posits that social networks, and the interactions between ... A Definition of Speech Community in Sociolinguistics Speech community is a term in sociolinguistics and linguistic anthropology used to describe a group of people who share the same language, speech characteristics, and ways of interpreting communication. Speech communities may be large regions like an urban area with a common, distinct accent (think of Boston with its dropped r's) or small units like families and friends (think of a nickname ... Speech community A speech community is a group of people who share a set of linguistic norms and expectations regarding the use of language. [ 1] It is a concept mostly associated with sociolinguistics and anthropological linguistics . Exactly how to define speech community is debated in the literature. Definitions of speech community tend to involve varying ... (PDF) The Speech Community The speech community (SpCom), a core concept in empirical linguistics, is the. intersection of many principal problems in sociolinguistic theory and method. I trace its history of development and ... A speech planning network for interactive language use In this study, we delineate the neural substrates underlying the planning processes relevant for rapid turn-taking by measuring cortical activity while participants engage in structured ... Speech community Definition & Meaning The meaning of SPEECH COMMUNITY is a group of people sharing characteristic patterns of vocabulary, grammar, and pronunciation. What is Communication Network: Examples, Types, & Importance A communication network refers to an interconnected system that enables the exchange and flow of information among individuals, teams, and departments. The communication network within an organization consists of various components such as hierarchies, departments, teams, and individuals, each with specific roles and responsibilities. Speech Community in Sociolinguistics| Definition, characteristics The concept of the speech community is one of the important concepts in sociolinguistics. In this video I have discussed what is speech communit... Speech Communities A community: a group of people with a shared set of activities, practices, beliefs, and social structures. A speech community: a group of people who share similar ideas, uses, and norms of language. The kind of group that sociolinguists attempt to study is called Speech Community. Network Definition & Meaning The meaning of NETWORK is a fabric or structure of cords or wires that cross at regular intervals and are knotted or secured at the crossings. How to use network in a sentence. ... Share the Definition of network on Twitter Twitter. Kids Definition. network. 1 of 2 noun. net· work ˈnet-ˌwərk . 1 How to represent speech as a network The algorithm ( netts) constructs networks from a speech transcript that represent the content of what the speaker said. The idea here is that the nodes in the network show the entities that the speaker mentioned, like a cat, a house, etc. (usually nouns). And the edges of the network show the relationships between the entities. What Is Speech Recognition? Speech recognition, also known as automatic speech recognition (ASR), computer speech recognition or speech-to-text, is a capability that enables a program to process human speech into a written format. While speech recognition is commonly confused with voice recognition, speech recognition focuses on the translation of speech from a verbal ... What is a Neural Network? Every neural network consists of layers of nodes, or artificial neurons—an input layer, one or more hidden layers, and an output layer. Each node connects to others, and has its own associated weight and threshold. If the output of any individual node is above the specified threshold value, that node is activated, sending data to the next ... Difference between speech, language and communication Communication. Communication refers to the process of exchanging information, including emotions and thoughts (Bishop and al., 2016), with others using speaking, writing, signs, facial expressions and body language. Communication thus incorporates speech and language, but also prosody (linguistic and emotional). Speech recognition Speech recognition is an interdisciplinary subfield of computer science and computational linguistics that develops methodologies and technologies that enable the recognition and translation of spoken language into text by computers. It is also known as automatic speech recognition (ASR), computer speech recognition or speech-to-text (STT).It incorporates knowledge and research in the computer ... Networking Definition & Meaning The meaning of NETWORKING is the exchange of information or services among individuals, groups, or institutions; specifically : the cultivation of productive relationships for employment or business. How to use networking in a sentence. What is Speech Analytics? (Definition, Benefits, Tools) A speech analyst is a specialist responsible for interpreting the data generated by speech analytics tools. Their primary tasks include analyzing call recordings, identifying trends and providing actionable insights to help businesses improve customer service, ensure compliance and optimize operational efficiency. speech network definition speech network translation in English - English Reverso dictionary, see also 'curtain speech, direct speech, free speech, indirect speech', examples, definition, conjugation network The Underground Railroad was a network of people and safe houses, not a railroad with tracks and trains. synonyms: organization, system similar words: association, league, order: definition 2: a group of radio or television stations, or a company that controls such a group. speech network Learn the definition of 'speech network'. Check out the pronunciation, synonyms and grammar. ... Using said short call number, the user establishes a link with the speech service network server and utilizes the designation of the service in the speech service network server (VOXP) to identify and start the service. patents-wipo. What is Speech Recognition? Automatic Speech Recognition (ASR) is a technology that enables computers to understand and transcribe spoken language into text. It works by analyzing audio input, such as spoken words, and converting them into written text, typically in real-time. ASR systems use algorithms and machine learning techniques to recognize and interpret speech ... Free Speech TV Free Speech TV (FSTV) is an American progressive news and opinion network.It was launched in 1995 and is owned and operated by Public Communicators Incorporated, a 501(c)(3) non-profit, tax-exempt organization founded in 1974. Distributed principally by Dish Network, DirecTV, and the network's live stream at freespeech.org and on Roku, Free Speech TV has run commercial free since 1995 with ... What does speech mean? Definition of speech in the Definitions.net dictionary. Meaning of speech. What does speech mean? Information and translations of speech in the most comprehensive dictionary definitions resource on the web. Login . The STANDS4 Network. ... A Member Of The STANDS4 Network. Harris explains in exclusive CNN interview why she's shifted her Vice President Kamala Harris on Thursday offered her most expansive explanation to date on why she's changed some of her positions on fracking and immigration, telling CNN's Dana Bash her ... assignment essay homework paper phd report resume review speech thesis