Computational models of speech pattern processing [proceedings of the NATO Advanced Study Institute on Computational Models of Speech Pattern Processing, held in St. Heliér, Jersey, UK, July 7 - 18, 1997]

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Format:	Tagungsbericht Buch
Sprache:	English
Veröffentlicht:	Berlin [u.a.] Springer 1999
Schriftenreihe:	NATO ASI series Ser. F, Computer and systems sciences ; 169
Schlagworte:	Reconnaissance automatique de la parole - Congrès Traitement automatique de la parole - Congrès Sprachverarbeitung Automatische Spracherkennung Mustererkennung Konferenzschrift > 1997 > Saint Hélier
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245	1	0	\|a Computational models of speech pattern processing \|b [proceedings of the NATO Advanced Study Institute on Computational Models of Speech Pattern Processing, held in St. Heliér, Jersey, UK, July 7 - 18, 1997] \|c ed. by Keith Ponting
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Datensatz im Suchindex

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adam_text	TABLE OF CONTENTS FOREWORD KEITH M. PONTING ..................................................................................................... VII 1. INSIGHT VS. PERFORMANCE .................................................................................... VIII 2. DANGERS ............................................................................................................. IX 3. HOT TOPICS ......................................................................................................... IX 4. TOWARDS THE FUTURE ............................................................................................ X 4.1 INTEGRATING KNOWLEDGE SOURCES .................................................................. XI 4.2 A UNIFIED THEORY? ....................................................................................... XI REFERENCES ............................................................................................................... XII SPEECH PATTERN PROCESSING ROGER K. MOORE ....................................................................................................... 1 1. THE STATE-OF-THE-ART IN SPEECH......................................................................... 1 2. SPEECH PATTERNING ............................................................................................. 2 3. SPEECH PATTERN PROCESSING ................................................................................ 3 4. WHITHER A UNIFIED THEORY? ............................................................................... 5 4.1 TOWARDSATHEORY ........................................................................................ 5 4.2 PRACTICAL ISSUES .......................................................................................... 5 5. WHAT WE KNOW ................................................................................................ 6 6. SOME THINGS WE DONT KNOW .......................................................................... 7 7. THE WAY FORWARD ............................................................................................. 7 REFERENCES ............................................................................................................... 8 PSYCHO-ACOUSTICS AND SPEECH PERCEPTION LOUIS C.W. POLS ....................................................................................................... 10 1. INTRODUCTION ...................................................................................................... 10 2. PSYCHO-ACOUSTICS .............................................................................................. 11 3. SPEECH PERCEPTION ............................................................................................ 13 3.1 VOWEL REDUCTION AND SCHWA ..................................................................... 13 3.2 SPECTRO-TEMPORAL DYNAMICS OF FORINANT TRANSITIONS ................................ 14 3.3 CONSONANT REDUCTION ................................................................................ 14 4. DISCUSSION ........................................................................................................ 15 REFERENCES ............................................................................................................... 16 XIV TABLE OF CONTENTS ACOUSTIC MODELLING FOR LARGE VOCABULARY CONTINUOUS SPEECH RECOGNITION STEVEYOUNG .............................................................................................................. 18 1. INTRODUCTION ...................................................................................................... 18 2. OVERVIEW OF LVCSR ARCHITECTURE.................................................................... 18 3. FRONT END PROCESSING ....................................................................................... 21 4. BASIC PHONE MODELLING .................................................................................... 22 4.1 HMM PHONE MODELS ................................................................................. 22 4.2 HMM PARAMETER ESTIMATION ..................................................................... 24 4.3 CONTEXT-DEPENDENT PHONE MODELS ........................................................... 26 5. ADAPTATION FOR LVCSR ..................................................................................... 30 5.1 MAXIMUM LIKELIHOOD LINEAR REGRESSION ................................................. 31 5.2 ESTIMATING THE MLLR TRANSFORMS ............................................................ 31 6. PROGRESS IN LVCSR........................................................................................... 33 7. DISCRIMINATIVE TRAINING FOR LVCSR ................................................................ 34 8. CONCLUSIONS ...................................................................................................... 37 REFERENCES ............................................................................................................... 38 TREE-BASED DEPENDENCE MODELS FOR SPEECH RECOGNITION MARI OSTENDORF, ASHVIN KANNAN AND ORITH RONEN .................................................. 40 1. INTRODUCTION ...................................................................................................... 40 2. HIDDEN TREE FRAMEWORK ................................................................................... 41 3. HIDDEN DEPENDENCE TREES ................................................................................ 43 3.1 THE MATHEMATICAL FRAMEWORK .................................................................. 43 3.2 APPLICATION TO SPEECH ............................................................................... 44 3.3 TOPOLOGY DESIGN AND PARAMETER ESTIMATION ............................................. 44 3.4 EXPERIMENTS............................................................................................... 46 4. MULTISCALE TREE PROCESSES ................................................................................ 47 4.1 THE MATHEMATICAL FRAMEWORK .................................................................. 47 4.2 APPLICATION TO SPEECH ............................................................................... 48 4.3 TOPOLOGY DESIGN AND PARAMETER ESTIMATION ............................................. 49 4.4 EXPERIMENTS............................................................................................... 50 5. DISCUSSION ........................................................................................................ 51 REFERENCES ............................................................................................................... 52 CONNECTIONIST AND HYBRID MODELS FOR AUTOMATIC SPEECH RECOGNITION JEAN-PAUL HATON ....................................................................................................... 54 1. INTRODUCTION ...................................................................................................... 54 2. A BRIEF OVERVIEW OF NEURAL NETWORKS ............................................................ 55 2.1 BASIC PRINCIPLES ......................................................................................... 55 2.2 MAIN MODELS FOR ASR ............................................................................... 56 3. SIGNAL PROCESSING AND FEATURE EXTRACTION USING ANNS ................................... 57 4. NEURAL NETWORKS AS STATIC PATTERN CLASSIFIERS .................................................. 58 4.1 SPEECH PATTERN CLASSIFICATION WITH PERCEPTRONS ........................................ 58 TABLE OF CONTENTS XV 4.2 FEATUREMAPS .............................................................................................. 58 5. DYNAMIC ASPECTS .............................................................................................. 59 5.1 POSITION OF THE PROBLEM ............................................................................. 59 5.2 TIME DELAYS .............................................................................................. 59 5.3 DYNAMIC CLASSIFIERS .................................................................................. 59 5.4 RECURRENT NNS ........................................................................................... 60 6. HYBRID MODELS.................................................................................................. 61 6.1 POSITION OF THE PROBLEM ............................................................................. 61 6.2 PROPOSED SOLUTIONS .................................................................................... 61 7. CONCLUSION ........................................................................................................ 63 REFERENCES ............................................................................................................... 63 COMPUTATIONAL MODELS FOR AUDITORY SPEECH PROCESSING LI DENG ................................................................................................................... 67 I. INTRODUCTION ...................................................................................................... 67 2. A NONLINEAR COMPUTATIONAL MODEL FOR BASILAR MEMBRANE WAVE MOTIONS .......... 67 3. FREQUENCY-DOMAIN AND TIME-DOMAIN COMPUTATIONAL SOLUTIONS TO THE BM MODEL ................................................................................................................ 68 4. INTERVAL ANALYSIS OF AUDITORY MODELS OUTPUTS FOR TEMPORAL INFORMATION EX- TRACTION .............................................................................................................. 70 5. IPIH REPRESENTATION OF CLEAN AND NOISY SPEECH SOUNDS .................................... 71 6. SPEECH RECOGNITION EXPERIMENTS ....................................................................... 73 7. SUMMARY AND DISCUSSIONS ................................................................................ 75 REFERENCES ............................................................................................................... 76 SPEAKER ADAPTATION OF CDHMMS USING BAYESIAN LEARNING CLAUDIO VAIR AND LUCIANO FISSORE ........................................................................... 78 1. INTRODUCTION ...................................................................................................... 78 2. BAYESIAN ESTIMATION OF CDHMMS .................................................................. 78 2.1 PRIOR DENSITY DEFINITION ............................................................................ 79 2.2 FORGETTING MECHANISM............................................................................... 79 2.3 PRIOR PARAMETER ESTIMATION AND MAP SOLUTION ........................................ 80 3. ACOUSTIC NORMALIZATION .................................................................................... 81 4. TASKS, CORPUS AND SYSTEM ................................................................................ 81 5. SPEAKER ADAPTATION EXPERIMENTS ..................................................................... 82 6. CONCLUSIONS ...................................................................................................... 83 REFERENCES ............................................................................................................... 83 DISCRIMINATIVE IMPROVEMENT OF THE REPRESENTATION SPACE FOR CONTINUOUS SPEECH RECOGNITION ANGEL DE LA TORRE, ANTONIO M. PEINADO, ANTONIO J. RUBIO, JOSE C. SEGURA ... ........ 84 1. INTRODUCTION ......................................................................................................... 84 2. DISCRIMINATIVE FEATURE EXTRACTION ....................................................................... 84 3. SGDFE ALGORITHM FOR CSR ................................................................................. 85 XVI TABLE OF CONTENTS 4. EXPERIMENTAL RESULTS ........................................................................................ 86 5. CONCLUSIONS ...................................................................................................... 89 REFERENCES ............................................................................................................... 89 DEALING WITH LOSS OF SYNCHRONISM IN MULTI-BAND CONTINUOUS SPEECH RECOGNITION SYSTEMS CHRISTOPHE CERISARA ................................................................................................. 90 1. INTRODUCTION ...................................................................................................... 90 2. FORCING SYNCHRONISM BETWEEN THE BANDS ....................................................... 91 2.1 FIRST APPROACH ........................................................................................... 91 2.2 EXPERIMENTS............................................................................................... 92 3. MODELING LOSS OF SYNCHRONISM ....................................................................... 92 3.1 THEORETICAL APPROACH ................................................................................ 92 3.2 EXPERIMENTAL APPROACH ............................................................................. 93 4. CONCLUSION ........................................................................................................ 94 REFERENCES ............................................................................................................... 95 K-NEAREST NEIGHBOURS ESTIMATOR IN A HMM-BASED RECOGNITION SYSTEM FABRICE LEFEVRE, CLAUDE MONTACIE AND MARIE-JOSE CARATY ..................................... 96 1. INTRODUCTION ...................................................................................................... 96 2. K-NN ASSESSMENT ............................................................................................ 96 3. K-NN ESTIMATOR IN HMM ................................................................................. 97 3.1 ADAPTATION PRINCIPLE.................................................................................. 98 3.2 HMM ESTIMATION IMPROVEMENT ................................................................ 98 4. EVALUATIONS ....................................................................................................... 99 4.1 RECOGNITION RATES ....................................................................................... 99 4.2 SNALC EVALUATION ................................................................................... 100 5. PERSPECTIVES ...................................................................................................... 101 REFERENCES ............................................................................................................... 101 ROBUST SPEECH RECOGNITION SADAOKIFURUI............................................................................................................ 102 1. MISMATCHES BETWEEN TRAINING AND TESTING ...................................................... 102 1.1 SPEECH VARIATION ........................................................................................ 102 1.2 INTER-SPEAKER VARIATION ............................................................................. 104 2. REDUCING MISMATCHES TO IMPROVE SPEECH RECOGNITION .................................. 104 2.1 PRINCIPLES OF ADAPTIVE SPEECH RECOGNITION .............................................. 104 2.2 THREE PRINCIPAL ADAPTATION METHODS FOR REDUCING MISMATCHES .............. 106 2.3 IMPORTANT PRACTICAL ISSUES ......................................................................... 107 2.4 N-BEST-BASED UNSUPERVISED ADAPTATION ................................................... 108 3. CONCLUSION ........................................................................................................ 109 REFERENCES ............................................................................................................... 109 TABLE OF CONTENTS XVII CHANNEL ADAPTATION KEITH M. PONTING ..................................................................................................... 112 I. INTRODUCTION ...................................................................................................... 112 1.1 MATCHED CONDITION TRAINING ....................................................................... 112 1.2 ROBUST FEATURES .......................................................................................... 112 1.3 MODEL ADAPTATION ....................................................................................... 113 1.4 CHANNEL ADAPTATION .................................................................................... 113 1.5 SPEECH ENHANCEMENT ................................................................................. 113 2. MODELS OF DISTORTION ......................................................................................... 113 2.1 MINIMUM MEAN SQUARE ERROR ..................................................................... 114 2.2 ADDITIVE NOISE ESTIMATION .......................................................................... 114 3. METHODS FOR CHANNEL ADAPTATION ....................................................................... 115 3.1 GLOBAL TRANSFORMATIONS .............................................................................. 115 3.2 CLASS-SPECIFIC CORRECTIONS .......................................................................... 116 3.3 EMPIRICAL METHODS BASED ON STEREO DATA ................................................... 117 3.4 MODEL-BASED COMPENSATION ....................................................................... 118 4. CONCLUSION ........................................................................................................ 119 REFERENCES ............................................................................................................... 120 SPEAKER CHARACTERIZATION, SPEAKER ADAPTATION AND VOICE CONVERSION SADAOKI FURUI........................................................................................................... 122 1. INTRODUCTION ...................................................................................................... 122 2. SPEAKER-CHARACTERIZATION ................................................................................. 122 3. SPEAKER RECOGNITION ........................................................................................ 123 4. SPEAKER-ADAPTATION TECHNIQUES FOR SPEECH RECOGNITION ................................ 124 4.1 CLASSIFICATION OF SPEAKER-ADAPTATIONLNORRNALIZATION METHODS ................ 124 4.2 SPEAKER CLUSTER SELECTION METHODS .......................................................... 124 4.3 INTERPOLATED RE-ESTIMATION ALGORITHM ...................................................... 125 4.4 SPECTRAL MAPPING ALGORITHM ..................................................................... 125 5. INDIVIDUALITY PROBLEMS IN SPEECH SYNTHESIS AND CODING ................................. 128 6. CONDUSION ......................................................................................................... 129 REFERENCES ............................................................................................................... 130 SPEAKER RECOGNITION SADAOKIFURUI............................................................................................................ 132 1. PRINCIPLES OF SPEAKER RECOGNITION ................................................................... 132 2. TEXT-INDEPENDENT SPEAKER RECOGNITION METHODS ............................................ 133 2.1 LONG-TERRN-STATISTICS-BASED METHODS ...................................................... 133 2.2 VQ-BASED METHODS ................................................................................... 135 2.3 ERGODIC-HMM-BASED METHODS ................................................................. 135 2.4 SPEECH-RECOGNITION-B ASED METHODS ....................................................... 136 3. TEXT-PROMPTED SPEAKER RECOGNITION ................................................................ 137 4. NORMALIZATION AND ADAPTATION TECHNIQUES ...................................................... 137 4.1 PARAMETER-DOMAIN NORMALIZATION ............................................................ 138 XVIII TABLE OF CONTENTS 4.2 LIKELIHOOD NORMALIZATION ......................................................................... 138 4.3 HMM ADAPTATION FOR NOISY CONDITIONS ................................................... 139 4.4 UPDATING MODELS AND A PRIORI THRESHOLD FOR SPEAKER VERIFICATION ... ...... 139 5. OPEN QUESTIONS AND CONCLUDING REMARKS ....................................................... 140 REFERENCES ............................................................................................................... 140 APPLICATION OF ACOUSTIC DISCRIMINATIVE TRAIMUG IN AN ERGODIC HMM FOR SPEAKER IDENTIFICATION LEANDRO RODRIGUEZ LI* NARES AND CARMEN GARCIA MATEO .......................................... 143 1. INTRODUCTION ...................................................................................................... 143 2. EXPERIMENTAL CONDITIONS .................................................................................. 144 3. SYSTEM ARCHITECTURE ......................................................................................... 145 3.1 ACOUSTIC SEGMENTATION.............................................................................. 145 3.2 THE PTH-HMM MODEL .............................................................................. 145 4. EXPERIMENTAL RESULTS ........................................................................................ 145 5. CONCLUSIONS ...................................................................................................... 147 REFERENCES ............................................................................................................... 148 COMPARISON OF SEVERAL COMPENSATION TECHNIQUES FOR ROBUST SPEAKER VERIFICATION LAURA DOCFO-FERNANDEZ AND CARMEN GARCIA-MATEO ............................................... 149 1. INTRODUCTION ...................................................................................................... 149 2. THE HMM RECOGNITION SYSTEM ......................................................................... 151 3. MISMATCH COMPENSATION TECHNIQUES ............................................................... 151 3.1 CMS .......................................................................................................... 151 3.2 5M1 ........................................................................................................... 152 3.3 SM2 ........................................................................................................... 152 4. EXPERIMENTS AND RESULTS .................................................................................. 152 5. DISCUSSION AND CONCLUSION .............................................................................. 156 REFERENCES ............................................................................................................... 156 SEGMENTAL ACOUSTIC MODELING FOR SPEECH RECOGNITION MAN OSTENDORF ......................................................................................................... 157 I. INTRODUCTION ...................................................................................................... 157 2. SEGMENTAL AND HIDDEN MARKOV MODELS ........................................................... 158 2.1 GENERAL MODELING FRAMEWORK .................................................................. 159 2.2 MODELS OF FEATURE DYNAMICS .................................................................... 161 3. RECOGNITION AND TRAINING ................................................................................. 165 3.1 RECOGNITION ALGORITHMS ............................................................................ 165 3.2 PARAMETER ESTITNATION ALGORITLITNS ............................................................. 166 4. SEGMENTAL FEATURES .......................................................................................... 168 5. SUNUNARY ........................................................................................................... 169 REFERENCES ............................................................................................................... 170 TABLE OF CONTCNTS XIX TRAJECTORY REPRESENTATIONS AND ACOUSTIC DESCRIPTIONS FOR A SEGMENT-MODELLING APPROACH TO AUTOMATIC SPEECH RECOGNITION WENDY J. HOLMES ..................................................................................................... 173 1. INTRODUCTION ...................................................................................................... 173 2. MODELLING TRAJECTORIES IN SPEECH ..................................................................... 174 3. REPRESENTING AN UNOBSERVED TRAJECTORY WITH SEGMENTAL HMMS .................... 175 3.1 CALCULATING SEGMENT PROBABILITIES ............................................................. 175 3.2 RECOGNITION EXPERIMENT ............................................................................ 176 4. HMM RECOGNITION WITH FORMANT FEATURES ...................................................... 177 5. MODELLING TRAJECTORIES OF CEPSTRUM AND FORMANT FEATURES ................................ 178 6. CONCLUSIONS ...................................................................................................... 178 REFERENCES ............................................................................................................... 179 SUPRASEGMENTAL MODELLING E. NOETH, A. BATLINER, A. KIESSLING, R. KOMPE AND H. NIEMANN ............................... 181 I. INTRODUCTION ...................................................................................................... 181 2. THE VERBMOBIL SYSTEM ..................................................................................... 183 3. COMPUTATION OF PROSODIC INFORMATION .............................................................. 183 3.1 EXTRACTION OF PROSODIC FEATURES ................................................................ 185 3.2 PROSODIC CLASSES ........................................................................................ 185 3.3 NEW BOUNDARY LABELS: THE SYNTACTIC-PROSODIC M-LABELS ........................ 186 3.4 CLASSIFICATION OF PROSODIC EVENTS.............................................................. 187 3.5 IMPROVING THE CLASSIFICATION RESULTS WITH STOCHASTIC LANGUAGE MODELS . 187 3.6 PROSODIC SCORING OF WHGS ........................................................................ 189 4. THE USE OF PROSODIC INFORMATION ..................................................................... 190 4.1 PROSODY AND SYNTAX * INTERACTION WILH THE TUG-GRAMMAR ...................... 190 4.2 PROSODY AND THE OTHER LINGUISTIC MODULES ............................................... 194 5. CONCLUDING REMARKS ........................................................................................ 196 REFERENCES ............................................................................................................... 196 COMPUTATIONAL MODELS FOR SPEECH PRODUCTION LI DENG ................................................................................................................... 199 1. INTRODUCTION ...................................................................................................... 199 2. SPEECH PRODUCTION MODELS IN SCIENCE/TECHNOLOGY LITERATURES ........................... 200 3. DERIVATION OF DISCRETE-TIME VERSION OF STATISTICAL TASK-DYNAMIC MODEL ............ 202 4. ALGORITHRNS FOR LEARNING TASK-DYNAMIC MODEL PARAMETERS AND FOR LIKELI- HOOD COMPUTATION ............................................................................................. 204 4.1 MODEL WITH DETERMINISTIC, TIME-INVARIANT PARAMETERS ............................... 205 4.2 MODEL WITH RANDOM, TIME-INVARIANT PARAMETERS ........................................ 207 4.3 MODEL WITH RANDOM, SMOOTHLY TIME-VARYING PARAMETERS .......................... 208 4.4 DISCRIMINATIVE LEARNING OF PRODUCTION MODELS* PARAMETERS ...................... 210 5. OTHER TYPES OF COMPUTATIONAL MODELS OF SPEECH PRODUCTION ............................ 210 6. SUMMARY AND DISCUSSIONS ................................................................................ 212 REFERENCES ............................................................................................................... 212 XX TABLE OF CONTEOTS ARTICULATORY FEATURES AND ASSOCIATED PRODUCTION MODELS IN STATISTICAL SPEECH RECOGNITION LI DENG ................................................................................................................... 214 1. INTRODUCTION ...................................................................................................... 214 2. FUNCTIONAL DESCRIPTION OF HUMAN SPEECH COMMUNICATION AS AN ENCODING- DECODING PROCESS .............................................................................................. 214 3. OVERVIEW OF THEORIES OF SPEECH PERCEPTION ...................................................... 215 4. A GENERAL FRAMEWORK OF STATISTICAL SPEECH RECOGNITION .................................... 216 5. BRIEF ANALYSIS OF WEAKNESSES OF CURRENT SPEECH RECOGNITION TECHNOLOGY ......... 217 6. PHONOLOGICAL MODEL: OVERLAPPING ARTICULATORY FEATURES AND RELATED HMMS ... 218 7. TASK-DYNAMIC MODEL OF SPEECH PRODUCTION ...................................................... 219 8. INTERFACING OVERLAPPING FEATURES TO TASK-DYNAMIC MODEL AND A GENERAL AR- CHITECTURE FOR SPEECH RECOGNITION ..................................................................... 220 9. DISCUSSIONS: MACHINE SPEECH RECOGNITION ........................................................ 220 REFERENCES ............................................................................................................... 223 TALKER NORMALIZATION WITH ARTICULATORY ANALYSIS-BY-SYNTHESIS RICHARD S. MCGOWAN .............................................................................................. 225 1. INTRODUCTION ...................................................................................................... 225 2. NORMALIZATION PROCEDURE .................................................................................. 226 3. EXPERIMENTS ...................................................................................................... 229 4. CONCLUSION ........................................................................................................ 231 REFERENCES ............................................................................................................... 231 THE PSYCHOLINGUISTICS OF SPOKEN WORD RECOGNITION CYNTHIA M. CONNINE AND THOMAS DEELMAN ............................................................ 233 1. INTRODUCTION ...................................................................................................... 233 2. OVERVIEW: MODELS OF SPOKEN WORD RECOGNITION ............................................... 233 3. CURRENCY OF MAPPING: UNITS AND THE NATURE OF LEXICAL REPRESENTATIONS ............. 235 4. TEMPORAL NATURE OF SPEECH: EARLY VS DELAYED COMMITMENT .............................. 237 4.1 DELAYED COMMITMENT ................................................................................ 238 5. MULTIPLE LEXICAL HYPOTHESES, LEXICAL COMPETITION AND GRADED ACTIVATION.......... 239 6. LANGUAGE ARCHITECTURE: LEXICAL AND SEGMENTAL LEVELS ...................................... 242 7. LANGUAGE ARCHITECTURE: LEXICAL AND SENTENTIAL ................................................. 244 8. CONTRIBUTION OF ATTENTION .................................................................................. 245 REFERENCES ............................................................................................................... 247 ISSUES IN USING MODELS FOR SELF EVALUATION AND CORRECTION OF SPEECH MARIE-CHRISTINE HATON ............................................................................................. 252 1. INTRODUCTION ...................................................................................................... 252 2. USING MODELS .................................................................................................... 253 3. NORM BUILDING .................................................................................................. 254 4. MATCHING BETWEEN THE SUBJECTS WORLD AND THE TECHNICAL WORLD ....................... 255 5. SETTLEMENT OF THE SPEECH EDUCATION PROGRAM .................................................... 256 TABLE OF CONTENIS XXI 6. MANAGEMENT OF THE EDUCATION PROGRAM ............................................................ 257 7. CONCLUSION ........................................................................................................ 257 REFERENCES ............................................................................................................... 257 THE USE OF THE MAXIMUM LIKELIHOOD CRITERION IN LANGUAGE MODELLING HERMANN NEY........................................................................................................... 259 1. INTRODUCTION ...................................................................................................... 259 2. PERPLEXITY AND MAXIMUM LIKELIHOOD .............................................................. 260 3. SMOOTHING AND DISCOUNTING FOR SPARSE DATA ................................................... 263 3.1 MODELFREE DISCOUNTING AND TURING-GOOD ESTIMATES ................................ 263 3.2 ABSOLUTE DISCOUNTING ................................................................................ 266 4. PARTITIONING-BASED MODELS ............................................................................... 267 4.1 EQUIVALENCE CLASSES OF HISTORIES AND DECISION TREES............................... 267 4.2 TWO-SIDED PARTITIONINGS AND WORD CLASSES .............................................. 270 5. WORD TRIGGER PAIRS ........................................................................................... 272 6. MAXIMUM ENTROPY APPROACH ........................................................................... 275 7. CONCLUSIONS ...................................................................................................... 277 REFERENCES ............................................................................................................... 277 LANGUAGE MODEL ADAPTATION RENATO DEMON AND MARCELLO FEDERICO ................................................................... 280 1. INTRODUCTION ...................................................................................................... 280 2. BACKGROUND ON LANGUAGE MODELS .................................................................... 281 3. ADAPTATION PARADIGMS ...................................................................................... 283 3.1 LM ADAPTATION IN DIALOGUE SYSTEMS........................................................... 284 4. BASIC STATISTICAL METHODS .................................................................................. 285 4.1 MAXIMUM A-POSTERIORI ESTIMATION ............................................................. 285 4.2 LINEAR INTERPOLATION ................................................................................... 286 4.3 SUBLANGUAGES MIXTURE ADAPTATION ............................................................. 288 4.4 BACKING-OFF ............................................................................................... 288 4.5 MAXIMUM ENTROPY .................................................................................... 290 4.6 MINIMUM DISCRIMINATION INFORMATION ...................................................... 291 4.7 GENERALIZED ITERATIVE SCALING ..................................................................... 292 4.8 CACHE MODEL AND WORD TRIGGERS................................................................. 293 5. PRACTICAL APPLICATIONS OF ADAPTATION PARADIGMS ................................................ 295 5.1 THE 1993 ARPA EVALUATION METHOD .......................................................... 295 5.2 MIXTURE BASED ADAPTATION .......................................................................... 296 5.3 ADAPTATION WITH A CACHE MODEL ................................................................. 298 5.4 ME AND MDI ADAPTATION............................................................................ 299 5.5 LM ADAPTATION IN INTERACTIVE SYSTEMS ....................................................... 299 6. CONCLUSION ........................................................................................................ 301 REFERENCES ............................................................................................................... 301 XXII TABLE OF CONTENTS USING NATURAL-LANGUAGE KNOWLEDGE SOURCES IN SPEECH RECOGNITION ROBERT C. MOORE ...................................................................................................... 304 1. INTRODUCTION ...................................................................................................... 304 2. ISSUES IN LANGUAGE MODELING FOR SPEECH RECOGNITION .................................... 305 3. FORMAL MODELS FOR NATURAL LANGUAGE .............................................................. 307 3.1 FINITE-STATE GRAMMARS .............................................................................. 307 3.2 CONTEXT-FREE GRAMMARS ............................................................................ 308 3.3 AUGMENTED CONTEXT-FREE GRAMMARS ........................................................ 309 3.4 EXPRESSIVE POWER OF GRAMMAR FORMALISMS AND THE REQUIREMENTS OF NATURAL LANGUAGE ...................................................................................... 310 4. SEARCH ARCHITECTURES FOR NATURAL-LANGUAGE-BASED LANGUAGE MODELS ............ 312 4.1 WORD LATTICE PARSING ................................................................................. 312 4.2 N -BEST FILTERING OR RESCORING .................................................................... 312 4.3 DYNAMIC GENERATION OF PARTIAL GRAMMAR NETWORKS ................................. 313 5. COMPILING UNIFICATION GRAMMARS INTO CONTEXT-FREE GRAMMARS .................... 314 5.1 INSTANTIATING UNIFICATION GRAMMARS .......................................................... 314 5.2 REMOVING LEFT RECURSION FROM CONTEXT-FREE GRAMMARS ........................ 316 6. ROBUST NATURAL-LANGUAGE-BASED LANGUAGE MODELS ........................................ 318 6.1 COMBINING LINGUISTICS AND STATISTICS IN A LANGUAGE MODEL ..................... 318 6.2 FUHY STATISTICAL NATURAL-LANGUAGE GRAMMARS ......................................... 320 7. SUMMARY .......................................................................................................... 325 REFERENCES ............................................................................................................... 326 HOW MAY I HELP YOU? A.L. GORIN, G. RICCARDI AND J.R. WRIGHT ................................................................. 328 1. INTRODUCTION ...................................................................................................... 328 2. A SPOKEN DIALOG SYSTEM.................................................................................. 329 3. DATABASE ........................................................................................................... 331 4. ALGORITHMS ........................................................................................................ 333 4.1 SALIENT FRAGMENT ACQUISITION .................................................................... 335 4.2 RECOGNIZING FRAGMENTS IN SPEECH ............................................................ 339 4.3 CALL CLASSIFICATION ..................................................................................... 341 5. EXPERIMENT RESULTS .......................................................................................... 343 6. CONCLUSIONS ...................................................................................................... 347 REFERENCES ............................................................................................................... 348 INTRODUCTION OF RULES INTO A STOCHASTIC APPROACH FOR LANGUAGE MODELLING THIERRY SPRIET AND MARC EL-BEZE ............................................................................ 350 1. INTRODUCTION ...................................................................................................... 350 2. STACK DECODING STRATEGY .................................................................................. 351 2.1 THEALGORITHM ............................................................................................ 351 2.2 THE EVALUATION FUNCTION ........................................................................... 351 2.3 PECULIAR ADVANTAGES OF THE ALGORITHM ...................................................... 352 TABLE OF CONTENTS XXIII 3. RULES ................................................................................................................. 353 3.1 CORRECTIONOFBIASES .................................................................................... 353 3.2 UNDER-REPRESENTED STRUCTURES AND LONG SPAN DEPENDENCIES ................... 353 4. MULTI LEVEL INTERACTIONS ................................................................................... 354 4.1 LINGUISTIC AND SYNTACTIC ............................................................................ 354 4.2 PHONOLOGY ................................................................................................. 354 5. CONCLUSION ........................................................................................................ 355 REFERENCES ............................................................................................................... 355 HISTORY INTEGRATION INTO SEMANTIC CLASSIFICATION MAURO CETTOLO AND ANNA CORAZZA ........................................................................... 356 1. INTRODUCTION ...................................................................................................... 356 2. CLASSIFIER ........................................................................................................... 357 3. DATA .................................................................................................................. 357 4. DIALOGUE HISTORY INTEGRATION ............................................................................ 358 5. DISCUSSION ........................................................................................................ 360 REFERENCES ............................................................................................................... 361 MULTILINGUAL SPEECH RECOGNITION E. NOETH AND S. HARBECK AND H. NIEMANN ................................................................ 362 1. INTRODUCTION ...................................................................................................... 362 2. ARCHITECTURE OF THE NATIONAL SQEL DEMONSTRATORS.......................................... 363 3. LANGUAGE IDENTIFICATION WITH DIFFERENT AMOUNTS OF KNOWLEDGE ABOUT THE TRAINING DATA .................................................................................................... 364 3.1 A SYSTEM WITH EXPLICIT LANGUAGE IDENTIFICATION ...................................... 365 3.2 A SYSTEM WITH HNPLICIT LANGUAGE IDENTIFICATION ....................................... 367 3.3 LANGUAGE IDENTIFICATION BASED ON CEPSTRAL FEATURE VECTORS .................... 369 4. RESULTS .............................................................................................................. 370 5. CONCLUSIONS AND FUTURE WORK .......................................................................... 373 REFERENCES ............................................................................................................... 373 TOWARD ALISP: A PROPOSAL FOR AUTOMATIC LANGUAGE INDEPENDENT SPEECH PROCESSING . GERARD CHOLLET, JAN C ERNOCK´ Y, ANDREI CONSTANTINESCU, SABINE DELIGNE AND FREDERIC BIMBOT ....................................................................................................... 375 1. INTRODUCTION ...................................................................................................... 375 2. PRACTICAL BENEFIT OF ALISP ................................................................................ 376 3. ISSUES SPECIFIC TO ALISP ................................................................................... 377 3.1 SELECTING FEATURES ...................................................................................... 377 3.2 MODELING SPEECH UNITS ............................................................................... 377 3.3 DEFINING A DERIVATION CRITERION .................................................................. 378 3.4 BUILDING A LEXICON ..................................................................................... 378 4. SOME TOOLS FOR ALISP ...................................................................................... 379 4.1 TEMPORAL DECOMPOSITION .......................................................................... 379 XXIV TABLE OF CONTENTS 4.2 THE MULTIGRAM MODEL ................................................................................ 380 5. EXPERIMENTS ...................................................................................................... 381 5.1 CROSS-LANGUAGE RECOGNITION .................................................................... 381 5.2 VERY LOW BIT RATE SPEECH CODING ................................................................ 382 5.3 MONO-SPEAKER CONTINUOUS SPEECH RECOGNITION ...................................... 384 6. CONCLUSIONS ...................................................................................................... 386 REFERENCES ............................................................................................................... 387 INTERACTIVE TRANSLATION OF CONVERSATIONAL SPEECH ALEX WAIBEL ............................................................................................................. 389 1. INTRODUCTION ...................................................................................................... 389 2. BACKGROUND ...................................................................................................... 390 2.1 THE PROBLEM OF SPOKEN LANGUAGE TRANSLATION ......................................... 390 2.2 RESEARCH EFFORTS ON SPEECH TRANSLATION.................................................... 391 3. JANUS-II - A CONVERSATIONAL SPEECH TRANSLATOR ............................................. 392 3.1 TASK DOMAINS AND DATA COLLECTION ........................................................... 392 3.2 SYSTEM DESCRIPTION ................................................................................... 394 3.3 PERFORMANCE EVALUATION ............................................................................ 398 4. APPLICATIONS AND FORMS OF DEPLOYMENT .......................................................... 400 4.1 INTERACTIVE DIALOG TRANSLATION ................................................................... 401 4.2 PORTABLE SPEECH TRANSLATION DEVICE.......................................................... 402 4.3 PASSIVE SIMULTANEOUS DIALOG TRANSLATION ................................................. 402 REFERENCES ............................................................................................................... 403 MULTIMODAL SPEECH SYSTEMS FRANCOISE D. NEEL AND WOLFGANG M. MINKER .......................................................... 404 1. INTRODUCTION ...................................................................................................... 404 2. SYSTEM ARCHITECTURE: KNOWLEDGE SOURCES AND CONTROLLERS ............................. 405 2.1 ENVIRONNIENT MODEL .................................................................................. 406 2.2 SYSTEM MODEL............................................................................................ 406 2.3 USER MODEL ................................................................................................ 408 2.4 TASK MODEL ................................................................................................ 410 2.5 DIALOGUE MODEL ......................................................................................... 411 2.6 MODELS INTERDEPENDENCY ........................................................................... 413 2.7 ROLE OF SPEECH IN MULTIMODAL APPLICATIONS ............................................. 413 3. INFORMATION SPEECH SYSTEMS ............................................................................ 414 3.1 SPONTANEOUS LANGUAGE CHARACTERISTICS ..................................................... 414 3.2 CASE GRAMMAR FORMALISM USED FOR TASK MODELLING ................................ 416 3.3 DIFFERENT PARSING METHODS ........................................................................ 417 3.4 TASK AND DIALOGUE MODEL INTEGRATION ....................................................... 426 4. CONCLUSION ........................................................................................................ 427 REFERENCES ............................................................................................................... 428 TABLE OF CONTENTS XXV MULTIMODAL INTERFACES FOR MULTIMEDIA INFORMATION AGENTS ALEX WAIBEL AND BERNHARD SUHM AND MINH TUE VO AND JIE YANG .......................... 431 1. INTRODUCTION ...................................................................................................... 431 2. INTERPRETATION OF MULTIMODAL INPUT .................................................................. 432 2.1 MULTIMODAL COMPONENTS ........................................................................... 432 2.2 JOINT INTERPRETATION .................................................................................... 432 3. MULTIMODAL ERROR CORRECTION ........................................................................... 433 3.1 MULTIMODAL INTERACTIVE ERROR REPAIR ......................................................... 433 3.2 ERROR REPAIR FOR MULTIMEDIA INFORMATION AGENTS ..................................... 433 3.3 EVALUATING INTERACTIVE ERROR REPAIR .......................................................... 434 4. MULTIMODAL INFORMATION AGENTS ....................................................................... 434 4.1 INFORMATION ACCESS.................................................................................... 434 4.2 INFORMATION CREATION ................................................................................. 435 4.3 INFORMATION MANIPULATION ......................................................................... 435 4.4 INFORMATION DISSEMINATION........................................................................ 436 4.5 CONTROLLING THE INTERFACE ........................................................................... 436 5. THE QUICKDOC APPLICATION ............................................................................... 437 6. CONCLUSIONS ...................................................................................................... 437 REFERENCES ............................................................................................................... 438 INDEX .............................................................................. ..................................... 440
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spelling	Computational models of speech pattern processing [proceedings of the NATO Advanced Study Institute on Computational Models of Speech Pattern Processing, held in St. Heliér, Jersey, UK, July 7 - 18, 1997] ed. by Keith Ponting Berlin [u.a.] Springer 1999 XXX, 446 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier NATO ASI series : Ser. F, Computer and systems sciences 169 Literaturangaben Reconnaissance automatique de la parole - Congrès ram Traitement automatique de la parole - Congrès ram Sprachverarbeitung (DE-588)4116579-2 gnd rswk-swf Automatische Spracherkennung (DE-588)4003961-4 gnd rswk-swf Mustererkennung (DE-588)4040936-3 gnd rswk-swf (DE-588)1071861417 Konferenzschrift 1997 Saint Hélier gnd-content Sprachverarbeitung (DE-588)4116579-2 s Mustererkennung (DE-588)4040936-3 s DE-604 Automatische Spracherkennung (DE-588)4003961-4 s Ponting, K. M. Sonstige oth Advanced Study Institute on Computational Models of Speech Pattern Processing 1997 Saint Hélier Sonstige (DE-588)2174168-2 oth NATO ASI series Ser. F, Computer and systems sciences ; 169 (DE-604)BV000013052 169 SWB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=008525962&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis
spellingShingle	Computational models of speech pattern processing [proceedings of the NATO Advanced Study Institute on Computational Models of Speech Pattern Processing, held in St. Heliér, Jersey, UK, July 7 - 18, 1997] NATO ASI series Reconnaissance automatique de la parole - Congrès ram Traitement automatique de la parole - Congrès ram Sprachverarbeitung (DE-588)4116579-2 gnd Automatische Spracherkennung (DE-588)4003961-4 gnd Mustererkennung (DE-588)4040936-3 gnd
subject_GND	(DE-588)4116579-2 (DE-588)4003961-4 (DE-588)4040936-3 (DE-588)1071861417
title	Computational models of speech pattern processing [proceedings of the NATO Advanced Study Institute on Computational Models of Speech Pattern Processing, held in St. Heliér, Jersey, UK, July 7 - 18, 1997]
title_auth	Computational models of speech pattern processing [proceedings of the NATO Advanced Study Institute on Computational Models of Speech Pattern Processing, held in St. Heliér, Jersey, UK, July 7 - 18, 1997]
title_exact_search	Computational models of speech pattern processing [proceedings of the NATO Advanced Study Institute on Computational Models of Speech Pattern Processing, held in St. Heliér, Jersey, UK, July 7 - 18, 1997]
title_full	Computational models of speech pattern processing [proceedings of the NATO Advanced Study Institute on Computational Models of Speech Pattern Processing, held in St. Heliér, Jersey, UK, July 7 - 18, 1997] ed. by Keith Ponting
title_fullStr	Computational models of speech pattern processing [proceedings of the NATO Advanced Study Institute on Computational Models of Speech Pattern Processing, held in St. Heliér, Jersey, UK, July 7 - 18, 1997] ed. by Keith Ponting
title_full_unstemmed	Computational models of speech pattern processing [proceedings of the NATO Advanced Study Institute on Computational Models of Speech Pattern Processing, held in St. Heliér, Jersey, UK, July 7 - 18, 1997] ed. by Keith Ponting
title_short	Computational models of speech pattern processing
title_sort	computational models of speech pattern processing proceedings of the nato advanced study institute on computational models of speech pattern processing held in st helier jersey uk july 7 18 1997
title_sub	[proceedings of the NATO Advanced Study Institute on Computational Models of Speech Pattern Processing, held in St. Heliér, Jersey, UK, July 7 - 18, 1997]
topic	Reconnaissance automatique de la parole - Congrès ram Traitement automatique de la parole - Congrès ram Sprachverarbeitung (DE-588)4116579-2 gnd Automatische Spracherkennung (DE-588)4003961-4 gnd Mustererkennung (DE-588)4040936-3 gnd
topic_facet	Reconnaissance automatique de la parole - Congrès Traitement automatique de la parole - Congrès Sprachverarbeitung Automatische Spracherkennung Mustererkennung Konferenzschrift 1997 Saint Hélier
url	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=008525962&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
volume_link	(DE-604)BV000013052
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Computational models of speech pattern processing [proceedings of the NATO Advanced Study Institute on Computational Models of Speech Pattern Processing, held in St. Heliér, Jersey, UK, July 7 - 18, 1997]

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