The Resource Fundamentals of speech recognition, Lawrence Rabiner, Biing-Hwang Juang

Fundamentals of speech recognition, Lawrence Rabiner, Biing-Hwang Juang

Label
Fundamentals of speech recognition
Title
Fundamentals of speech recognition
Statement of responsibility
Lawrence Rabiner, Biing-Hwang Juang
Creator
Contributor
Subject
Language
eng
Member of
Action
committed to retain for EAST
Cataloging source
DLC
http://library.link/vocab/creatorDate
1943-
http://library.link/vocab/creatorName
Rabiner, Lawrence R.
Illustrations
illustrations
Index
index present
LC call number
TK7895.S65
LC item number
R33 1993
Literary form
non fiction
Nature of contents
bibliography
http://library.link/vocab/relatedWorkOrContributorName
Juang, B. H.
http://library.link/vocab/subjectName
  • Automatic speech recognition
  • Speech processing systems
  • Otomatik ses tanıma
  • Konuşma işleme sistemleri
  • modèle Markov caché
  • perception parole
  • traitement signal
  • reconnaissance automatique
  • traitement parole
  • reconnaissance parole
  • Reconnaissance automatique de la parole
  • Traitement automatique de la parole
  • Automatic speech recognition
  • Speech processing systems
  • Spraakherkenning
  • Automatische Spracherkennung
  • Reconhecimento de voz (engenharia eletrica)
  • Teoria da computacao
  • Reconnaissance automatique de la parole
  • Traitement automatique de la parole
  • Automatische Spracherkennung
Label
Fundamentals of speech recognition, Lawrence Rabiner, Biing-Hwang Juang
Link
https://archive.org/details/fundamentalsofsp00rabi
Instantiates
Publication
Bibliography note
Includes bibliographical references and index
Carrier category
volume
Carrier category code
  • nc
Carrier MARC source
rdacarrier
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
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
Dimensions
25 cm.
Extent
xxxv, 507 pages
Isbn
9780130151575
Lccn
92034093
Media category
unmediated
Media MARC source
rdamedia
Media type code
  • n
Other physical details
illustrations
System control number
  • (OCoLC)26674087
  • (OCoLC)ocm26674087
Label
Fundamentals of speech recognition, Lawrence Rabiner, Biing-Hwang Juang
Link
https://archive.org/details/fundamentalsofsp00rabi
Publication
Bibliography note
Includes bibliographical references and index
Carrier category
volume
Carrier category code
  • nc
Carrier MARC source
rdacarrier
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
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
Dimensions
25 cm.
Extent
xxxv, 507 pages
Isbn
9780130151575
Lccn
92034093
Media category
unmediated
Media MARC source
rdamedia
Media type code
  • n
Other physical details
illustrations
System control number
  • (OCoLC)26674087
  • (OCoLC)ocm26674087

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