Fundamentals of speech recognition:
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Upper Saddle River, New Jersey
Prentice Hall PTR
1993
|
| Schriftenreihe: | Prentice Hall signal processing series
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | xxxv, 507 Seiten Diagramme |
| ISBN: | 0130151572 9780130151575 |
Internformat
MARC
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| 245 | 1 | 0 | |a Fundamentals of speech recognition |c Lawrence Rabiner ; Biing-Hwang Juang |
| 264 | 1 | |a Upper Saddle River, New Jersey |b Prentice Hall PTR |c 1993 | |
| 300 | |a xxxv, 507 Seiten |b Diagramme | ||
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| 650 | 4 | |a Reconnaissance automatique de la parole | |
| 650 | 7 | |a Reconnaissance automatique de la parole |2 ram | |
| 650 | 4 | |a Traitement automatique de la parole | |
| 650 | 7 | |a modèle Markov caché |2 inriac | |
| 650 | 7 | |a perception parole |2 inriac | |
| 650 | 7 | |a reconnaissance automatique |2 inriac | |
| 650 | 7 | |a reconnaissance parole |2 inriac | |
| 650 | 7 | |a traitement parole |2 inriac | |
| 650 | 7 | |a traitement signal |2 inriac | |
| 650 | 4 | |a Automatic speech recognition | |
| 650 | 4 | |a Speech processing systems | |
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Datensatz im Suchindex
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|---|---|
| adam_text | FUNDAMENTALS
OF SPEECH
RECOGNITION
Lawrence Rabiner
Biing-Hwang Juang
PTR Prentice Hall
Englewood Cliffs, New Jersey 07632
CONTENTS
LIST OF FIGURES
LIST OF TABLES
PREFACE
1 FUNDAMENTALS OF SPEECH RECOGNITION
1 1 Introduction
1 2 The Paradigm for Speech Recognition
1 3 Outline
14A Brief History of Speech-Recognition Research
2 THE SPEECH SIGNAL: PRODUCTION, PERCEPTION, AND
ACOUSTIC-PHONETIC CHARACTERIZATION
2 1 Introduction
211 The Process of Speech Production and Perception in Human
Beings
2 2 The Speech-Production Process
2 3 Representing Speech in the Time and Frequency Domains
2 4 Speech Sounds and Features
viii Contents
241 The Vowels
242 Diphthongs
243 Semivowels
244 Nasal Consonants
245 Unvoiced Fricatives
246 Voiced Fricatives
247 Voiced and Unvoiced Stops
248 Review Exercises
2 5 Approaches to Automatic Speech Recognition by Machine
251 Acoustic-Phonetic Approach to Speech Recognition
252 Statistical Pattern-Recognition Approach to Speech
Recognition
253 Artificial Intelligence (AI) Approaches to Speech
Recognition
254 Neural Networks and Their Application to Speech
Recognition
2 6 Summary
3 SIGNAL PROCESSING AND ANALYSIS METHODS FOR SPEECH
RECOGNITION
3 1 Introduction
311 Spectral Analysis Models
3 2 The Bank-of-Filters Front-End Processor
321 Types of Filter Bank Used for Speech Recognition
322 Implementations of Filter Banks
323 Summary of Considerations for Speech-Recognition Filter
Banks
324 Practical Examples of Speech-Recognition Filter Banks
325 Generalizations of Filter-Bank Analyzer
3 3 Linear Predictive Coding Model for Speech Recognition
331 The LPC Model
332 LPC Analysis Equations
333 The Autocorrelation Method
334 The Covariance Method
335 Review Exercise
336 Examples of LPC Analysis
337 LPC Processor for Speech Recognition
338 Review Exercises
339 Typical LPC Analysis Parameters
3 4 Vector Quantization
341 Elements of a Vector Quantization Implementation
342 The VQ Training Set
343 The Similarity or Distance Measure
344 Clustering the Training Vectors
345 Vector Classification Procedure
346 Comparison of Vector and Scalar Quantizers
Contents ix
347 Extensions of Vector Quantization 129
348 Summary of the VQ Method 131
3 5 Auditory-Based Spectral Analysis Models 132
351 The EIH Model 134
3 6 Summary 139
4 PATTERN-COMPARISON TECHNIQUES 141
4 1 Introduction
4 2 Speech (Endpoint) Detection
4 3 Distortion Measures—Mathematical Considerations
4 4 Distortion Measures—Perceptual Considerations
4 5 Spectral-Distortion Measures
451 Log Spectral Distance
452 Cepstral Distances
453 Weighted Cepstral Distances and Liftering
454 Likelihood Distortions
455 Variations of Likelihood Distortions
456 Spectral Distortion Using a Warped Frequency Scale
457 Alternative Spectral Representations and Distortion
Measures
458 Summary of Distortion Measures—Computational
Considerations
4 6 Incorporation of Spectral Dynamic Features into the Distortion
Measure
4 7 Time Alignment and Normalization
471 Dynamic Programming—Basic Considerations
472 Time-Normalization Constraints
473 Dynamic Time-Warping Solution
474 Other Considerations in Dynamic Time Warping
475 Multiple Time-Alignment Paths
4 8 Summary
5 SPEECH RECOGNITION SYSTEM DESIGN AND IMPLEMENTATION
ISSUES
5 1 Introduction
5 2 Application of Source-Coding Techniques to Recognition
521 Vector Quantization and Pattern Comparison Without Time
Alignment
522 Centroid Computation for VQ Codebook Design
523 Vector Quantizers with Memory
524 Segmental Vector Quantization
525 Use of a Vector Quantizer as a Recognition Preprocessor
526 Vector Quantization for Efficient Pattern Matching
5 3 Template Training Methods
531 Casual Training
x Contents
532 Robust Training
533 Clustering
5 4 Performance Analysis and Recognition Enhancements
541 Choice of Distortion Measures
542 Choice of Clustering Methods and kNN Decision Rule
543 Incorporation of Energy Information
544 Effects of Signal Analysis Parameters
545 Performance of Isolated Word-Recognition Systems
5 5 Template Adaptation to New Talkers
551 Spectral Transformation
552 Hierarchical Spectral Clustering
5 6 Discriminative Methods in Speech Recognition
561 Determination of Word Equivalence Classes
562 Discriminative Weighting Functions
563 Discriminative Training for Minimum Recognition Error
5 7 Speech Recognition in Adverse Environments
571 Adverse Conditions in Speech Recognition
572 Dealing with Adverse Conditions
5 8 Summary
6 THEORY AND IMPLEMENTATION OF HIDDEN MARKOV MODELS 321
6 1 Introduction
6 2 Discrete-Time Markov Processes
6 3 Extensions to Hidden Markov Models
631 Coin-Toss Models
632 The Um-and-Ball Model
633 Elements of an HMM
634 HMM Generator of Observations
6 4 The Three Basic Problems for HMMs
641 Solution to Problem 1—Probability Evaluation
642 Solution to Problem 2—Optimal State Sequence
643 Solution to Problem 3—Parameter Estimation
644 Notes on the Reestimation Procedure
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 12 1 Scaling
6 12 2 Multiple Observation Sequences
6 12 3 Initial Estimates of HMM Parameters
Contents xi
6 12 4 Effects of Insufficient Training Data 370
6 12 5 Choice of Model 371
6 13 Improving the Effectiveness of Model Estimates 372
6 13 1 Deleted Interpolation 372
6 13 2 Bayesian Adaptation 373
6 13 3 Corrective Training 376
6 14 Model Clustering and Splitting 377
6 15 HMM System for Isolated Word Recognition 378
6 15 1 Choice of Model Parameters 379
6 15 2 Segmental K-Means Segmentation into States 382
6 15 3 Incorporation of State Duration into the HMM 384
6 15 4 HMM Isolated-Digit Performance 385
6 16 Summary 386
7 SPEECH RECOGNITION BASED ON CONNECTED WORD MODELS 390
7 1 Introduction 390
7 2 General Notation for the Connected Word-Recognition
Problem 393
7 3 The Two-Level Dynamic Programming (Two-Level DP)
Algorithm 395
731 Computation of the Two-Level DP Algorithm 399
7 4 The Level Building (LB) Algorithm 400
741 Mathematics of the Level Building Algorithm 401
742 Multiple Level Considerations 405
743 Computation of the Level Building Algorithm 407
744 Implementation Aspects of Level Building 410
745 Integration of a Grammar Network 414
746 Examples of LB Computation of Digit Strings 416
7 5 The One-Pass (One-State) Algorithm 416
7 6 Multiple Candidate Strings 420
7 7 Summary of Connected Word Recognition Algorithms 423
7 8 Grammar Networks for Connected Digit Recognition 425
7 9 Segmental K-Means Training Procedure 427
7 10 Connected Digit Recognition Implementation 428
7 10 1 HMM-Based System for Connected Digit Recognition 429
7 10 2 Performance Evaluation on Connected Digit Strings 430
7 11 Summary 432
8 LARGE VOCABULARY CONTINUOUS SPEECH RECOGNITION 434
8 1 Introduction
8 2 Subword Speech Units
8 3 Subword Unit Models Based on HMMs
8 4 Training of Subword Units
xii Contents
8 5 Language Models for Large Vocabulary Speech
Recognition 447
8 6 Statistical Language Modeling 448
8 7 Perplexity of the Language Model 449
8 8 Overall Recognition System Based on Subword Units 450
881 Control of Word Insertion/Word Deletion Rate 454
882 Task Semantics 454
883 System Performance on the Resource Management Task 454
8 9 Context-Dependent Subword Units 458
891 Creation of Context-Dependent Diphones and Triphones 460
892 Using Interword Training to Create CD Units 461
893 Smoothing and Interpolation of CD PLU Models 462
894 Smoothing and Interpolation of Continuous Densities 464
895 Implementation Issues Using CD Units 464
896 Recognition Results Using CD Units 467
897 Position Dependent Units 469
898 Unit Splitting and Clustering 470
899 Other Factors for Creating Additional Subword Units 475
8 9 10 Acoustic Segment Units 476
8 10 Creation of Vocabulary-Independent Units 477
8 11 Semantic Postprocessor for Recognition 478
8 12 Summary 478
TASK ORIENTED APPLICATIONS OF AUTOMATIC SPEECH
RECOGNITION 482
9 1 Introduction 482
9 2 Speech-Recognizer Performance Scores 484
9 3 Characteristics of Speech-Recognition Applications 485
931 Methods of Handling Recognition Errors 486
9 4 Broad Classes of Speech-Recognition Applications 487
9 5 Command-and-Control Applications 488
951 Voice Repertory Dialer 489
952 Automated Call-Type Recognition 490
953 Call Distribution by Voice Commands 491
954 Directory Listing Retrieval 491
955 Credit Card Sales Validation 492
9 6 Projections for Speech Recognition 493
INDEX 497
|
| any_adam_object | 1 |
| author | Rabiner, Lawrence R. 1943- Juang, Biing-Hwang |
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| discipline | Biologie Informatik Elektrotechnik Elektrotechnik / Elektronik / Nachrichtentechnik |
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| illustrated | Not Illustrated |
| indexdate | 2024-07-09T17:22:50Z |
| institution | BVB |
| isbn | 0130151572 9780130151575 |
| language | English |
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| spelling | Rabiner, Lawrence R. 1943- Verfasser (DE-588)138833737 aut Fundamentals of speech recognition Lawrence Rabiner ; Biing-Hwang Juang Upper Saddle River, New Jersey Prentice Hall PTR 1993 xxxv, 507 Seiten Diagramme txt rdacontent n rdamedia nc rdacarrier Prentice Hall signal processing series Reconnaissance automatique de la parole Reconnaissance automatique de la parole ram Traitement automatique de la parole modèle Markov caché inriac perception parole inriac reconnaissance automatique inriac reconnaissance parole inriac traitement parole inriac traitement signal inriac Automatic speech recognition Speech processing systems Automatische Spracherkennung (DE-588)4003961-4 gnd rswk-swf Matériel didactique Automatische Spracherkennung (DE-588)4003961-4 s DE-604 Juang, Biing-Hwang Verfasser (DE-588)138833826 aut HEBIS Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=005709766&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Rabiner, Lawrence R. 1943- Juang, Biing-Hwang Fundamentals of speech recognition Reconnaissance automatique de la parole Reconnaissance automatique de la parole ram Traitement automatique de la parole modèle Markov caché inriac perception parole inriac reconnaissance automatique inriac reconnaissance parole inriac traitement parole inriac traitement signal inriac Automatic speech recognition Speech processing systems Automatische Spracherkennung (DE-588)4003961-4 gnd |
| subject_GND | (DE-588)4003961-4 |
| title | Fundamentals of speech recognition |
| title_auth | Fundamentals of speech recognition |
| title_exact_search | Fundamentals of speech recognition |
| title_full | Fundamentals of speech recognition Lawrence Rabiner ; Biing-Hwang Juang |
| title_fullStr | Fundamentals of speech recognition Lawrence Rabiner ; Biing-Hwang Juang |
| title_full_unstemmed | Fundamentals of speech recognition Lawrence Rabiner ; Biing-Hwang Juang |
| title_short | Fundamentals of speech recognition |
| title_sort | fundamentals of speech recognition |
| topic | Reconnaissance automatique de la parole Reconnaissance automatique de la parole ram Traitement automatique de la parole modèle Markov caché inriac perception parole inriac reconnaissance automatique inriac reconnaissance parole inriac traitement parole inriac traitement signal inriac Automatic speech recognition Speech processing systems Automatische Spracherkennung (DE-588)4003961-4 gnd |
| topic_facet | Reconnaissance automatique de la parole Traitement automatique de la parole modèle Markov caché perception parole reconnaissance automatique reconnaissance parole traitement parole traitement signal Automatic speech recognition Speech processing systems Automatische Spracherkennung Matériel didactique |
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