Robustness in automatic speech recognition: fundamentals and applications
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | Undetermined |
| Veröffentlicht: |
Boston [u.a.]
Kluwer Acad. Publ.
1996
|
| Schriftenreihe: | The Kluwer international series in engineering and computer science : VLSI, computer architecture and digital signal processing
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XXX, 440 S. Ill., graph. Darst. |
| ISBN: | 0792396464 |
Internformat
MARC
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| 245 | 1 | 0 | |a Robustness in automatic speech recognition |b fundamentals and applications |c by Jean-Claude Junqua ; Jean-Paul Haton |
| 264 | 1 | |a Boston [u.a.] |b Kluwer Acad. Publ. |c 1996 | |
| 300 | |a XXX, 440 S. |b Ill., graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a The Kluwer international series in engineering and computer science : VLSI, computer architecture and digital signal processing | |
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Datensatz im Suchindex
| _version_ | 1804125084120514560 |
|---|---|
| adam_text | Contents
vii
TABLE OF CONTENTS
About the authors
xxiii
Foreword
xxv
Preface
xxvii
Acknowledgments
xxix
Part A SPEECH COMMUNICATION BY HUMANS
AND MACHINES
..................................................1
Chapter
1
NATURE AND PERCEPTION OF SPEECH
SOUNDS
.........................................................................3
1.1
SPEECH PRODUCTION
........................................................4
1.1.1
The speech apparatus
.................................................................4
1.1.2
Articulatory phonetics
...............................................................5
1.1.3
Articulatory models
...................................................................6
1.1.4
Production of speech in noise
....................................................7
1.2
ACOUSTIC PHONETICS
.......................................................9
1.2.1
Representations of speech
.........................................................9
1.2.2
Phonemes and allophones
........................................................10
1.2.3
Vowels
.....................................................................................11
1.2.4
Consonants
..............................................................................15
1.2.5
Acoustic-phonetic changes due to the Lombard reflex
...........20
1.3
HEARING AND PERCEPTION
...........................................24
1.3.1
The auditory system
................................................................24
1.3.2
Perception of sounds
................................................................27
1.3.3
Influence of the Lombard reflex on speech perception
...........30
Chapter
2
BACKGROUND ON SPEECH ANALYSIS
...........37
2.1
PRINCIPLES AND AIMS OF SPEECH ANALYSIS METH¬
ODS
........................................................................................38
2.1.1
Introduction
.............................................................................38
2.1.2
The Fourier transforms
............................................................39
2.1.3
Digital filter-banks
...................................................................41
2.2
SPEECH ANALYSIS BASED ON A PRODUCTION MOD-
ROBUSTNESS IN AUTOMATIC SPEECH RECOGNITION
Contents
EL ...........................................................................................41
2.2.1
Introduction to the linear prediction analysis
..........................41
2.2.2
The LPC Model
.......................................................................42
2.2.3
Spectral modeling using LPC
..................................................44
2.3
FEATURE ANALYSIS
.........................................................45
2.3.1
Introduction
.............................................................................45
2.3.2
Typical LPC parameters used in recognition
..........................45
2.3.3
Vector quantization
.................................................................49
2.4
TIME-FREQUENCY REPRESENTATIONS OF SPEECH
. 49
2.5
WAVELETS
...........................................................................51
2.6
HIGHER-ORDER SPECTRAL ANALYSIS
........................54
2.7
SPEECH ANALYSIS BASED ON AUDITORY MODELS
56
2.7.1
Introduction
.............................................................................56
2.7.2
Physiological and psychoacoustic models
...............................58
2.7.3
Application to ASR
.................................................................61
2.8
LIMITS OF STANDARD ANALYSES IN PRESENCE OF
NOISE
....................................................................................62
Chapter
3
FUNDAMENTALS OF AUTOMATIC SPEECH
RECOGNITION
..........................................................73
3.1
PRELIMINARIES
..................................................................74
3.1.1
Basic principles
.......................................................................74
3.1.2
Historical background
.............................................................77
3.2
DISTANCE MEASURES
......................................................80
3.2.1
Introduction
.............................................................................80
3.2.2
Spectral distance measures
......................................................81
3.2.3
Distance measures and speech perception
...............................83
3.3
PATTERN RECOGNITION METHODS FOR ASR
............84
3.3.1
Basic principles
.......................................................................84
3.3.2
Time normalization
.................................................................85
3.3.3
Stochastic modeling
.................................................................90
3.3.4
Neural networks
.....................................................................102
3.4
SPEAKER-DEPENDENT AND SPEAKER-INDEPENDENT
RECOGNITION
...................................................................112
3.4.1
Introduction
...........................................................................112
3.4.2
Template selection in pattern recognition ASR systems
.......113
3.5
PERFORMING FINE DISTINCTIONS IN ASR
................113
Contents
Part
В
ROBUSTNESS EM ASR: PROBLEMS AND IS¬
SUES
....................................................................125
Chapter
4
SPEAKER VARIABILITY AND SPECIFICITY
. 127
4.1
VARIANTS OF SPEECH AND SPEAKING STYLES
......128
4.1.1
Introduction
...........................................................................128
4.1.2
Read versus spontaneous speech
...........................................132
4.1.3
Stress and emotion in speech
.................................................132
4.1.4
Male-female differences
........................................................134
4.1.5
Voice conversion
...................................................................135
4.1.6
Available databases to study speaking styles
........................137
4.2
VARIABILITY AND
INVARIANCE
.................................138
4.2.1
Preliminaries
..........................................................................138
4.2.2
Personal variation or intra-speaker variability
......................141
4.2.3
Inter-speaker variability
.........................................................142
4.2.4
Environment variability
.........................................................143
4.2.5
Linguistic variability
.............................................................144
4.2.6
Contextual variation
..............................................................145
4.2.7
Robust phonetic features in the presence of noise
.................146
4.2.8
Relational
invariance
.............................................................147
Chapter
5
DEALING WITH NOISY SPEECH AND CHANNEL
DISTORTIONS
.........................................................155
5.1
TYPICAL NOISE SOURCES AND CHANNEL DISTOR¬
TIONS
..................................................................................156
5.1.1
Preliminaries
..........................................................................156
5.1.2
Signal-to-noise ratio evaluation
.............................................158
5.1.3
General assumptions
..............................................................160
5.1.4
Characteristics of some common noises
................................161
5.2
EFFECTS OF ADDITIVE NOISE ON SPEECH
................167
5.3
HUMAN PERFORMANCE FOR SPEECH IN NOISE
......168
5.4
SOME ISSUES IN ASR OF NOISY SPEECH
...................171
5.4.1
Introduction and specific difficulties
.....................................171
5.4.2 Endpoint
detection
.................................................................173
5.5
THE LOMBARD REFLEX AND
ГГЅ
INCIDENCE ON ASR
SYSTEMS
............................................................................179
5.5.1
Preliminaries
..........................................................................179
5.5.2
ASR of Lombard speech
.......................................................180
ROBUSTNESS IN AUTOMATIC SPEECH RECOGNITION
Contents
PartC
POSSIBLE SOLUTIONS
AND SOME
PER¬
SPECTIVES
.......................................................191
Chapter
6
THE CURRENT
TECHNOLOGY
AND ITS LIMITS:
AN OVERVIEW
.......................................................193
6.1
INTRODUCTION
................................................................194
6.2
WHEREWEARETODAYANDWHERETECHNOLOGYIS
HEADING
............................................................................194
6.2.1
Current technology................................................................
194
6.2.2
Real challenges
......................................................................196
6.2.3
Some reasons for today s limitations
....................................200
6.3
SPEECH RECOGNITION BY HUMAN LISTENERS AND
MACHINES
.........................................................................201
6.4
OVERVIEW OF RECENT ADVANCES IN ROBUST
SPEECH PROCESSING
......................................................203
Chapter
7
TOWARDS ROBUST SPEECH ANALYSIS
.........207
7.1
PRELIMINARIES
................................................................208
7.2
SIGNAL ACQUISITION
.....................................................208
7.3
ROBUST SPEECH ANALYSIS
..........................................212
7.3.1
On the use of auditory models for better speech analysis
.....212
7.3.2
Robust spectral estimation and
ARMA
models
....................225
Chapter
8
ON THE USE OF A ROBUST SPEECH REPRESEN¬
TATION
.....................................................................233
8.1
INTRODUCTION
................................................................234
8.2
FEATURE EXTRACTION
..................................................235
8.2.1
Time derivatives of speech
....................................................235
8.2.2 AR
modeling in the autocorrelation domain
.........................241
8.2.3
Feature processing
.................................................................243
8.2.4
Feature transformation
...........................................................250
8.2.5
Feature estimation in noise
....................................................254
8.2.6
Other techniques providing improved features
.....................255
8.3
NOISE-ROBUST DISTORTION AND SIMILARITY MEA¬
SURES
..................................................................................255
8.3.1
Cepstral lifters
.......................................................................255
8.3.2
Robust distortion measures
....................................................257
8.3.3
Discriminative similarity measures
.......................................261
Contents
Chapter
9 ASR
OF NOISY, STRESSED, AND CHANNEL DIS¬
TORTED SPEECH
...................................................273
9.1
INTRODUCTION
................................................................274
9.2
SPEECH ENHANCEMENT
................................................276
9.2.1
Filtering techniques
...............................................................276
9.2.2
Signal estimation techniques based on statistical modeling for
speech enhancement
..............................................................279
9.2.3
Linear and non-linear spectral subtraction
............................281
9.2.4
Signal restoration via a mapping transformation
...................286
9.3
MODEL COMPENSATION
................................................290
9.3.1
HMM
composition and decomposition
.................................290
9.3.2
Noise masking, data contamination, and noise immunity learn¬
ing
..........................................................................................294
9.3.3
Adaptation techniques for noisy speech recognition
.............
29S
9.3.4
Minimum error training
.........................................................303
9.3.5
Stress and channel compensation
..........................................307
9.3.6
Concluding remarks
...............................................................312
Chapter
10
WORD-SPOTTING AND REJECTION
................325
10.1
WORD-SPOTTING VERSUS ENDPOINT-BASED RECOG¬
NITION
................................................................................326
10.1.1
Preliminaries
..........................................................................326
10.1.2
Template matching word-spotters
.........................................329
10.1.3
Training garbage (or filler) models
.......................................330
10.1.4
Word-spotting and large vocabulary recognition
..................332
50.1.
S
Vocabulary-independent word-spotting and user-defined key¬
words
.....................................................................................332
10.1.6
Performance measures
...........................................................333
10.1.7
Post word-spotting processing and rejection
.........................334
10.1.8
Examples of word-spotting applications
...............................337
10.2
CONFIDENCE MEASURES AND THE NEW WORD PROB¬
LEM
......................................................................................338
10.2.1
Recognition confidence measures
.........................................338
10.2.2
Detecting out-of-vocabulary words and adding new words..
339
Chapter
11
SPONTANEOUS SPEECH
......................................347
11.1
INTRODUCTION
................................................................348
11.2
THE ATIS DATABASE AND SPONTANEOUS SPEECH
ROBUSTNESS IN AUTOMATIC SPEECH RECOGNITION
Contents
CORPORA...........................................................................351
11.3 THE SPEECH RECOGNTnON-NATURAL
LANGUAGE
IN¬
TERFACE ............................................................................353
11.4
THE LANGUAGE
MODEL................................................356
11.5 ROBUST
PARSING AND
INTERPRETATION................360
Chapter
12
ON THE USE OF KNOWLEDGE IN ASR
............371
12.1
STATEMENT OF THE PROBLEM
....................................372
12.2
HYBRID MODELS FOR ASR
............................................373
12.2.1
Preliminaries
..........................................................................373
12.2.2
Hybrid data-based approaches
...............................................374
12.2.3
ORION: A hybrid system for isolated word recognition
......377
12.3
MODELS FOR COOPERATION BETWEEN KNOWLEDGE
SOURCES
............................................................................379
12.3.1
Statement of the problem
.......................................................379
12.3.2
Bottom-up versus top-down processing
................................379
12.3.3
Heterarchical models for ASR
...............................................380
12.4
DEDUCTIVE AND ABDUCTTVE REASONING MODELS
FOR ASR
..............................................................................381
12.4.1
Use of a production rule model
.............................................381
12.4.2
Truth maintenance and abduction
.........................................384
12.5
CONCLUSION
....................................................................388
Chapter
13
APPLICATION DOMAIN, HUMAN FACTORS,
AND DIALOGUE
.....................................................393
13.1
THE APPLICATION DOMAIN
..........................................394
13.2
HUMAN FACTORS AND USER INTERFACE
................396
13.3
DIALOGUE FOR IMPROVED ROBUSTNESS
................398
13.3.1
Beyond sentences and turn talking: towards a natural interac¬
tion
.........................................................................................398
13.3.2
Dialogue context and error correction
...................................399
13.3.3 Multimodal
dialogue systems
................................................400
13.3.4
Different dialogue strategies for different applications
.........401
13.4
APmCATION-INDEPENDENCE AND FAST PROTOTYP¬
ING
.......................................................................................404
13.4.1
Introduction
...........................................................................404
13.4.2
Vocabulary-independent recognition
....................................404
13.4.3
Application-independent dialogue strategies
........................407
13.4.4
The notion of global speech interface
...................................408
13.5
THE ASSESSMENT AND
ГГЅ
DIFFICULTIES
................409
Contents
13.6
A
ROBUST
REAL-WORLD
APPLICATION....................412
13.6.1
Introduction
...........................................................................412
13.6.2 TOBIE-SOL:
A conversational system for a weak-sighted oper¬
ator
.........................................................................................413
13.7
APPLICATION PERSPECTIVES FOR THE YEAR
2000 421
Appendix
...........................................................................................429
Index
..................................................................................................431
ROBUSTNESS IN AUTOMATIC SPEECH RECOGNITION
|
| any_adam_object | 1 |
| author | Junqua, Jean-Claude Haton, Jean-Paul |
| author_facet | Junqua, Jean-Claude Haton, Jean-Paul |
| author_role | aut aut |
| author_sort | Junqua, Jean-Claude |
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| building | Verbundindex |
| bvnumber | BV010609715 |
| classification_rvk | ZN 6070 |
| classification_tum | ELT 533f DAT 714f |
| ctrlnum | (OCoLC)246512314 (DE-599)BVBBV010609715 |
| discipline | Informatik Elektrotechnik Elektrotechnik / Elektronik / Nachrichtentechnik |
| format | Book |
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| id | DE-604.BV010609715 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T17:55:56Z |
| institution | BVB |
| isbn | 0792396464 |
| language | Undetermined |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-007077993 |
| oclc_num | 246512314 |
| open_access_boolean | |
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| owner_facet | DE-29T DE-91 DE-BY-TUM DE-91G DE-BY-TUM DE-355 DE-BY-UBR |
| physical | XXX, 440 S. Ill., graph. Darst. |
| publishDate | 1996 |
| publishDateSearch | 1996 |
| publishDateSort | 1996 |
| publisher | Kluwer Acad. Publ. |
| record_format | marc |
| series2 | The Kluwer international series in engineering and computer science : VLSI, computer architecture and digital signal processing |
| spelling | Junqua, Jean-Claude Verfasser aut Robustness in automatic speech recognition fundamentals and applications by Jean-Claude Junqua ; Jean-Paul Haton Boston [u.a.] Kluwer Acad. Publ. 1996 XXX, 440 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier The Kluwer international series in engineering and computer science : VLSI, computer architecture and digital signal processing Sprache (DE-588)4056449-6 gnd rswk-swf Robustheit (DE-588)4126481-2 gnd rswk-swf Sprachanalyse (DE-588)4129916-4 gnd rswk-swf Signalverarbeitung (DE-588)4054947-1 gnd rswk-swf Automatische Spracherkennung (DE-588)4003961-4 gnd rswk-swf Automatische Spracherkennung (DE-588)4003961-4 s Robustheit (DE-588)4126481-2 s DE-604 Signalverarbeitung (DE-588)4054947-1 s Sprache (DE-588)4056449-6 s Sprachanalyse (DE-588)4129916-4 s 1\p DE-604 Haton, Jean-Paul Verfasser aut Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=007077993&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Junqua, Jean-Claude Haton, Jean-Paul Robustness in automatic speech recognition fundamentals and applications Sprache (DE-588)4056449-6 gnd Robustheit (DE-588)4126481-2 gnd Sprachanalyse (DE-588)4129916-4 gnd Signalverarbeitung (DE-588)4054947-1 gnd Automatische Spracherkennung (DE-588)4003961-4 gnd |
| subject_GND | (DE-588)4056449-6 (DE-588)4126481-2 (DE-588)4129916-4 (DE-588)4054947-1 (DE-588)4003961-4 |
| title | Robustness in automatic speech recognition fundamentals and applications |
| title_auth | Robustness in automatic speech recognition fundamentals and applications |
| title_exact_search | Robustness in automatic speech recognition fundamentals and applications |
| title_full | Robustness in automatic speech recognition fundamentals and applications by Jean-Claude Junqua ; Jean-Paul Haton |
| title_fullStr | Robustness in automatic speech recognition fundamentals and applications by Jean-Claude Junqua ; Jean-Paul Haton |
| title_full_unstemmed | Robustness in automatic speech recognition fundamentals and applications by Jean-Claude Junqua ; Jean-Paul Haton |
| title_short | Robustness in automatic speech recognition |
| title_sort | robustness in automatic speech recognition fundamentals and applications |
| title_sub | fundamentals and applications |
| topic | Sprache (DE-588)4056449-6 gnd Robustheit (DE-588)4126481-2 gnd Sprachanalyse (DE-588)4129916-4 gnd Signalverarbeitung (DE-588)4054947-1 gnd Automatische Spracherkennung (DE-588)4003961-4 gnd |
| topic_facet | Sprache Robustheit Sprachanalyse Signalverarbeitung Automatische Spracherkennung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=007077993&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT junquajeanclaude robustnessinautomaticspeechrecognitionfundamentalsandapplications AT hatonjeanpaul robustnessinautomaticspeechrecognitionfundamentalsandapplications |