Non-Linguistic Analysis of Call Center Conversations: SpringerBriefs in Speech Technology

Call center conversations are a rich source of information that can be used to improve customer satisfaction, agent performance, and overall business operations. Traditional methods of analyzing call center conversations have focused on linguistic features, such as the words that are spoken and the grammar that is used. However, recent research has shown that non-linguistic features, such as the tone of voice, the pitch of the voice, and the speed of speech, can also provide valuable insights into the conversation.

Non-Linguistic Analysis of Call Center Conversations (SpringerBriefs in Electrical and Computer Engineering)

by Sunil Kumar Kopparapu

5 out of 5

Language	:	English
File size	:	3249 KB
Text-to-Speech	:	Enabled
Screen Reader	:	Supported
Enhanced typesetting	:	Enabled
Print length	:	96 pages

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Non-linguistic analysis of call center conversations is a rapidly growing field, and this book provides a comprehensive overview of the latest research in this area. The book includes chapters on various aspects of non-linguistic analysis, such as emotion recognition, speaker diarization, and language identification, as well as applications of non-linguistic analysis in call center settings, such as customer satisfaction analysis and agent performance evaluation.

Emotion Recognition

Emotion recognition is one of the most important aspects of non-linguistic analysis of call center conversations. The ability to accurately recognize the emotions of customers and agents can help businesses to improve customer satisfaction, resolve conflicts, and provide better support. There are a variety of different methods that can be used to recognize emotions from speech, including acoustic features, prosodic features, and linguistic features.

Acoustic features are physical characteristics of speech, such as the pitch, the loudness, and the duration. Prosodic features are features that relate to the intonation and rhythm of speech. Linguistic features are features that relate to the words that are spoken and the grammar that is used.

The most common method of emotion recognition from speech is to use acoustic features. Acoustic features are relatively easy to extract from speech, and they have been shown to be effective in recognizing a variety of different emotions. However, prosodic features and linguistic features can also provide valuable information for emotion recognition.

Speaker Diarization

Speaker diarization is the process of identifying and tracking the different speakers in a conversation. Speaker diarization is important for a variety of applications, such as customer identification, fraud detection, and meeting summarization. There are a variety of different methods that can be used for speaker diarization, including acoustic features, prosodic features, and linguistic features.

Acoustic features are physical characteristics of speech, such as the pitch, the loudness, and the duration. Prosodic features are features that relate to the intonation and rhythm of speech. Linguistic features are features that relate to the words that are spoken and the grammar that is used.

The most common method of speaker diarization is to use acoustic features. Acoustic features are relatively easy to extract from speech, and they have been shown to be effective in identifying and tracking speakers. However, prosodic features and linguistic features can also provide valuable information for speaker diarization.

Language Identification

Language identification is the process of determining the language that is being spoken in a conversation. Language identification is important for a variety of applications, such as machine translation, speech recognition, and customer support. There are a variety of different methods that can be used for language identification, including acoustic features, prosodic features, and linguistic features.

Acoustic features are physical characteristics of speech, such as the pitch, the loudness, and the duration. Prosodic features are features that relate to the intonation and rhythm of speech. Linguistic features are features that relate to the words that are spoken and the grammar that is used.

The most common method of language identification is to use acoustic features. Acoustic features are relatively easy to extract from speech, and they have been shown to be effective in identifying a variety of different languages. However, prosodic features and linguistic features can also provide valuable information for language identification.

Applications of Non-Linguistic Analysis in Call Center Settings

Non-linguistic analysis can be used for a variety of applications in call center settings, including:

• Customer satisfaction analysis
• Agent performance evaluation
• Fraud detection
• Customer identification
• Meeting summarization

Customer satisfaction analysis is the process of measuring the level of satisfaction that customers have with a product or service. Non-linguistic analysis can be used to measure customer satisfaction by analyzing the emotions of customers during call center conversations. This information can be used to identify areas where customer satisfaction can be improved.

Agent performance evaluation is the process of assessing the performance of call center agents. Non-linguistic analysis can be used to evaluate agent performance by analyzing the emotions of customers and agents during call center conversations. This information can be used to identify areas where agent performance can be improved.

Fraud detection is the process of identifying fraudulent activities. Non-linguistic analysis can be used to detect fraud by analyzing the emotions of customers and agents during call center conversations. This information can be used to identify suspicious activities that may indicate fraud.

Customer identification is the process of identifying customers. Non-linguistic analysis can be used to identify customers by analyzing the emotions of customers during call center conversations. This information can be used to personalize the customer experience and provide better support.

Meeting summarization is the process of summarizing the key points of a meeting. Non-linguistic analysis can be used to summarize meetings by analyzing the emotions of participants during the meeting. This information can be used to identify the most important points of the meeting and create a summary that is easy to understand.

Non-linguistic analysis of call center conversations is a rapidly growing field with a wide range of applications. This book provides a comprehensive overview of the latest research in this area, and it offers a detailed account of the methods that are used for non-linguistic analysis. This book will be of interest to researchers, practitioners, and students in the fields of speech technology, natural language processing, and human-computer interaction.

Non-Linguistic Analysis of Call Center Conversations (SpringerBriefs in Electrical and Computer Engineering)

by Sunil Kumar Kopparapu

5 out of 5

Language	:	English
File size	:	3249 KB
Text-to-Speech	:	Enabled
Screen Reader	:	Supported
Enhanced typesetting	:	Enabled
Print length	:	96 pages

FREE