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Fundamentals of speech recognition /

by Rabiner, Lawrence R; Juang, B. H. (Biing-Hwang).
Material type: materialTypeLabelBookSeries: Prentice Hall signal processing series. Publisher: New Delhi : Englewood Cliffs, N.J. : Dorling Kindersley (India) Private Limited, PTR Prentice Hall, 2007. c1993Description: 543 p. : ill. ; 25 cm.ISBN: 8131705129; 0130151572; 9780130151575.Subject(s): Automatic speech recognition | Speech processing systemsOnline resources: WorldCat details
Contents:
Table of contents 1. Fundamentals of Speech Recognition. 1.2. The Paradigm for Speech Recognition. 1.3. Outline. 1.4. A Brief History of Speech-Recognition Research -- 2. The Speech Signal: Production, Perception, and Acoustic-Phonetic Characterization. 2.2. The Speech-Production Process. 2.3. Representing Speech in the Time and Frequency Domains. 2.4. Speech Sounds and Features. 2.5. Approaches to Automatic Speech Recognition by Machine -- 3. Signal Processing and Analysis Methods for Speech Recognition. 3.2. The Bank-of-Filters Front-End Processor. 3.3. Linear Predictive Coding Model for Speech Recognition. 3.4. Vector Quantization. 3.5. Auditory-Based Spectral Analysis Models -- 4. Pattern-Comparison Techniques. 4.2. Speech (Endpoint) Detection. 4.3. Distortion Measures -- Mathematical Considerations. 4.4. Distortion Measures -- Perceptual Considerations. 4.5. Spectral-Distortion Measures. 4.6. Incorporation of Spectral Dynamic Features into the Distortion Measure. 4.7. Time Alignment and Normalization -- 5. Speech Recognition System Design and Implementation Issues. 5.2. Application of Source-Coding Techniques to Recognition. 5.3. Template Training Methods. 5.4. Performance Analysis and Recognition Enhancements. 5.5. Template Adaptation to New Talkers. 5.6. Discriminative Methods in Speech Recognition. 5.7. Speech Recognition in Adverse Environments -- 6. Theory and Implementation of Hidden Markov Models. 6.2. Discrete-Time Markov Processes. 6.3. Extensions to Hidden Markov Models. 6.4. The Three Basic Problems for HMMs. 6.5. Types of HMMs. 6.6. Continuous Observation Densities in HMMs. 6.7. Autoregressive HMMs. 6.8. Variants on HMM Structures -- Null Transitions and Tied States. 6.9. Inclusion of Explicit State Duration Density in HMMs. 6.10. Optimization Criterion -- ML, MMI, and MDI. 6.11. Comparisons of HMMs. 6.12. Implementation Issues for HMMs. 6.13. Improving the Effectiveness of Model Estimates. 6.14. Model Clustering and Splitting. 6.15. HMM System for Isolated Word Recognition -- 7. Speech Recognition Based on Connected Word Models. 7.2. General Notation for the Connected Word-Recognition Problem. 7.3. The Two-Level Dynamic Programming (Two-Level DP) Algorithm. 7.4. The Level Building (LB) Algorithm. 7.5. The One-Pass (One-State) Algorithm. 7.6. Multiple Candidate Strings. 7.7. Summary of Connected Word Recognition Algorithms. 7.8. Grammar Networks for Connected Digit Recognition. 7.9. Segmental K-Means Training Procedure. 7.10. Connected Digit Recognition Implementation -- 8. Large Vocabulary Continuous Speech Recognition. 8.2. Subword Speech Units. 8.3. Subword Unit Models Based on HMMs. 8.4. Training of Subword Units. 8.5. Language Models for Large Vocabulary Speech Recognition. 8.6. Statistical Language Modeling. 8.7. Perplexity of the Language Model. 8.8. Overall Recognition System Based on Subword Units. 8.9. Context-Dependent Subword Units. 8.10. Creation of Vocabulary-Independent Units. 8.11. Semantic Postprocessor for Recognition -- 9. Task Oriented Applications of Automatic Speech Recognition. 9.2. Speech-Recognizer Performance Scores. 9.3. Characteristics of Speech-Recognition Applications. 9.4. Broad Classes of Speech-Recognition Applications. 9.5. Command-and-Control Applications. 9.6. Projections for Speech Recognition.
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Includes bibliographical references and index.

Table of contents 1. Fundamentals of Speech Recognition. 1.2. The Paradigm for Speech Recognition. 1.3. Outline. 1.4. A Brief History of Speech-Recognition Research --
2. The Speech Signal: Production, Perception, and Acoustic-Phonetic Characterization. 2.2. The Speech-Production Process. 2.3. Representing Speech in the Time and Frequency Domains. 2.4. Speech Sounds and Features. 2.5. Approaches to Automatic Speech Recognition by Machine --
3. Signal Processing and Analysis Methods for Speech Recognition. 3.2. The Bank-of-Filters Front-End Processor. 3.3. Linear Predictive Coding Model for Speech Recognition. 3.4. Vector Quantization. 3.5. Auditory-Based Spectral Analysis Models --
4. Pattern-Comparison Techniques. 4.2. Speech (Endpoint) Detection. 4.3. Distortion Measures --
Mathematical Considerations. 4.4. Distortion Measures --
Perceptual Considerations. 4.5. Spectral-Distortion Measures. 4.6. Incorporation of Spectral Dynamic Features into the Distortion Measure. 4.7. Time Alignment and Normalization --
5. Speech Recognition System Design and Implementation Issues. 5.2. Application of Source-Coding Techniques to Recognition. 5.3. Template Training Methods. 5.4. Performance Analysis and Recognition Enhancements. 5.5. Template Adaptation to New Talkers. 5.6. Discriminative Methods in Speech Recognition. 5.7. Speech Recognition in Adverse Environments --
6. Theory and Implementation of Hidden Markov Models. 6.2. Discrete-Time Markov Processes. 6.3. Extensions to Hidden Markov Models. 6.4. The Three Basic Problems for HMMs. 6.5. Types of HMMs. 6.6. Continuous Observation Densities in HMMs. 6.7. Autoregressive HMMs. 6.8. Variants on HMM Structures --
Null Transitions and Tied States. 6.9. Inclusion of Explicit State Duration Density in HMMs. 6.10. Optimization Criterion --
ML, MMI, and MDI. 6.11. Comparisons of HMMs. 6.12. Implementation Issues for HMMs. 6.13. Improving the Effectiveness of Model Estimates. 6.14. Model Clustering and Splitting. 6.15. HMM System for Isolated Word Recognition --
7. Speech Recognition Based on Connected Word Models. 7.2. General Notation for the Connected Word-Recognition Problem. 7.3. The Two-Level Dynamic Programming (Two-Level DP) Algorithm. 7.4. The Level Building (LB) Algorithm. 7.5. The One-Pass (One-State) Algorithm. 7.6. Multiple Candidate Strings. 7.7. Summary of Connected Word Recognition Algorithms. 7.8. Grammar Networks for Connected Digit Recognition. 7.9. Segmental K-Means Training Procedure. 7.10. Connected Digit Recognition Implementation --
8. Large Vocabulary Continuous Speech Recognition. 8.2. Subword Speech Units. 8.3. Subword Unit Models Based on HMMs. 8.4. Training of Subword Units. 8.5. Language Models for Large Vocabulary Speech Recognition. 8.6. Statistical Language Modeling. 8.7. Perplexity of the Language Model. 8.8. Overall Recognition System Based on Subword Units. 8.9. Context-Dependent Subword Units. 8.10. Creation of Vocabulary-Independent Units. 8.11. Semantic Postprocessor for Recognition --
9. Task Oriented Applications of Automatic Speech Recognition. 9.2. Speech-Recognizer Performance Scores. 9.3. Characteristics of Speech-Recognition Applications. 9.4. Broad Classes of Speech-Recognition Applications. 9.5. Command-and-Control Applications. 9.6. Projections for Speech Recognition.

Electrical & Electronic Engineering

Tahur Ahmed

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