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THE ELECTRICAL ENGINEERINGAND APPLIED SIGNAL PROCESSING SERIES
Edited by Alexander Poularikas
The Advanced Signal Processing Handbook:Theory and Implementation for Radar, Sonar,
and Medical Imaging Real-Time SystemsStergios Stergiopoulos
The Transform and Data Compression HandbookK.R. Rao and P.C. Yip
Handbook of Multisensor Data FusionDavid Hall and James Llinas
Handbook of Neural Network Signal ProcessingYu Hen Hu and Jenq-Neng Hwang
Handbook of Antennas in Wireless CommunicationsLal Chand Godara
Noise Reduction in Speech ApplicationsGillian M. Davis
Signal Processing NoiseVyacheslav P. Tuzlukov
Digital Signal Processing with Examples in MATLAB®
Samuel Stearns
Applications in Time-Frequency Signal ProcessingAntonia Papandreou-Suppappola
The Digital Color Imaging HandbookGaurav Sharma
Pattern Recognition in Speech and Language ProcessingWu Chou and Biing Huang Juang
Forthcoming Titles
Propagation Data Handbook for Wireless Communication System DesignRobert Crane
Smart AntennasLal Chand Godara
Nonlinear Signal and Image Processing: Theory, Methods, and ApplicationsKenneth Barner and Gonzalo R. Arce
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Forthcoming Titles (continued)
Soft Computing with MATLAB®
Ali Zilouchian
Signal and Image Processing Navigational SystemsVyacheslav P. Tuzlukov
Wireless Internet: Technologies and ApplicationsApostolis K. Salkintzis and Alexander Poularikas
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CRC PR ESSBoca Raton London New York Washington, D.C.
Edited byWU CHOUAvaya Labs Research
BIING HWANG JUANGGeorgia Institute of Technology
PATTERNRECOGNITION inSPEECH andLANGUAGEPROCESSING
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Preface
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Basking Ridge, New JerseySeptember, 2002
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Contributors
A. Abella
James Allan
T. Alonso
Jerome R. Bellegarda
William Byrne
Wu Chou
Sadaoki Furui
Jean-Luc Gauvain
Vaibhava Goel
Allen L. Gorin
Qiang Huo
Biing-Hwang Juang
Shigeru Katagiri
Lori Lamel
Qi (Peter) Li
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John Makhoul
Hermann Ney
F. J. Och
G. Riccardi
Richard M. Schwartz
J. H. Wright
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Contents
1 Minimum Classification Error (MCE) Approach in Pattern RecognitionWu Chou
2 Minimum Bayes-Risk Methods in Automatic Speech RecognitionVaibhava Goel� and William Byrne� � �
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3 A Decision Theoretic Formulation for Robust Automatic Speech Recog-nitionQiang Huo
4 Speech Pattern Recognition using Neural NetworksShigeru Katagiri
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5 Large Vocabulary Speech Recognition Based on Statistical MethodsJean-Luc Gauvain and Lori Lamel
6 Toward Spontaneous Speech Recognition and UnderstandingSadaoki Furui
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7 Speaker AuthenticationQi Li� and Biing-Hwang Juang� � �
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8 HMMs for Language Processing ProblemsRichard M. Schwartz and John Makhoul
9 Statistical Language Models With Embedded Latent Semantic Knowl-edgeJerome R. Bellegarda
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10 Semantic Information Processing of Spoken Language – How May I
Help You?sm
A. L. Gorin, A. Abella, T. Alonso, G. Riccardi, and J. H. Wright,
11 Machine Translation Using Statistical ModelingHerman Ney, and F. J. Och
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12 Modeling Topics for Detection and TrackingJames Allan
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1
Minimum Classification Error (MCE)Approach in Pattern Recognition
Wu ChouAvaya Labs Research, Avaya Inc., USA
CONTENTS
1.1 Introduction
Proceedings of The IEEE �
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1.2 Optimal Classifier from Bayes Decision Theory
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1.3 Discriminant Function Approach to Classifier Design
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1.5.1 MCE Classifier Design Strategy
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FIGURE 1.2A structure diagram of a context dependent head-body-tail digit model inspeech recognition.
1.6.1 String Model Based MCE Approach
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FIGURE 1.3A diagram of the embedded string model based MCE training process.
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1.6.2.1 Discriminative Model Combination
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1.6.2.2 Discriminative Language Model Estimation
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FIGURE 1.4Block diagram of a speaker verification system
1.7.1 Speaker Verification and Identification
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Acknowledgement
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IEEE Transactions on Speech and Audio Processing
IEEE Proc. ICASSP’98
Proc.ICASSP’96
Speech Commu-nication
IEEE Transactions on AutomaticControl
IEEEProc. ICASSP’99
IEEE Proc.ICASSP’96
Proc. ICASSP-2002
IEEE Transactions on Image Processing
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2
Minimum Bayes-Risk Methods in AutomaticSpeech Recognition
Vaibhava Goel� and William Byrne�
�IBM; �Johns Hopkins University
CONTENTS
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2.1 Minimum Bayes-Risk Classification Framework
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2.2 Practical MBR Procedures for ASR
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2.2.1 Summation over Hidden State Sequences
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2.2.2 MBR Recognition with N-best Lists
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FIGURE 2.1An example lattice. The time marks correspond to the node times and theword ending times. The numbers on the edges are logarithms of conditionaljoint probabilities as described in the text. The partial path log-probability ofa partial hypothesis is the log of the probability of its path; the partial path�� � (‘HELLO’,‘0.6’) in this lattice has value ����. The lattice backwardlog-probability of a partial hypothesis �� is the log of the sum of probabili-ties of all lattice paths from end node of �� to the lattice end node; for thepartial path �� � (‘HELLO’,‘0.6’) in this lattice these paths are indicated bydotted lines and the lattice backward log-probability of this �� is �����. Thelattice total probability of a partial path is the exponentiated sum of its partialpath log-probability and lattice backward log-probability; its value is ����� for�� � (‘HELLO’, ‘0.6’) in the lattice above.
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2.2.3.2 �� Search Under General Loss Functions
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2.3.2 ROVER
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FIGURE 2.2An example word transition network.
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FIGURE 2.3Joining two correspondence sets.
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2.4 Experimental Results
2.4.1 Parameter Tuning within the MBR Classification Rule
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TABLE 2.1
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2.4.2.1 Likelihood Scale Factor Tuning
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TABLE 2.2
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2.4.3 ROVER and e-ROVER for Multilingual ASR
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FIGURE 2.4Top panel shows the ratio of total number of e-ROVER correspondence sets tothat of ROVER correspondence sets, as a function of the pinching threshold.Bottom panel shows the WER performance of e-ROVER for these thresholds.
2.4.3.1 Correspondence Set Pinching
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2.5 Summary
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2.6 Acknowledgements
References
Mathematical Statistics: Basic Ideas andSelected topics
IEEE Conference on Acoustics, Speech, and Signal Pro-cessing
��� Hub-5 Conversational Speech Recognition Workshop
In Proceedings of the NIST and NSASpeech Transcription Workshop
IEEE Workshop on Au-tomatic Speech Recognition and Understanding
ACL99
IEEE Conference on Acous-tics, Speech, and Signal Processing
Word List With Content Word Marks
Minimum Bayes-Risk Automatic Speech Recognition
�� Eurospeech-99
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In Proceedings of the NIST and NSA Speech Transcription Work-shop
Computer Speech and Language
Research Notes No. 40, Center for Language andSpeech Processing
IEEE Conference on Acous-tics, Speech, and Signal Processing
��� International Conference on Spoken Language Pro-
cessing
IEEE Conferenceon Acoustics, Speech, and Signal Processing
IEEE Transactions on Systems Scienceand Cybernetics
SIGART Newsletter
IBM Journalof Research Development
Statistical Methods for Speech Recognition
Proceedings of the 1997 Large Vocabulary Continuous Speech RecognitionWorkshop
IEEE Transactions on Signal Processing
��� International
Conference on Spoken Language Processing
IEEE
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Conference on Acoustics, Speech, and Signal Processing
1997 IEEE Workshopon Automatic Speech Recognition and Understanding
Soviet Phys. Dokl.
Eurospeech-99
9th Hub-5 Conversational Speech Recognition Work-shop
9th Hub-5 Conversational Speech RecognitionWorkshop
Eurospeech-95
IEEE Transactions on Acoustics, Speech,and Signal Processing
IEEE Transactions on Acoustics, Speech, and Signal Processing
��
IEEE Conference onAcoustics, Speech, and Signal Processing
IEEE Trans. PAMI
IEEE Conference on Acoustics, Speech, and Signal Processing
Eurospeech-97
Estimation of Dependences Based on Empirical Data
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IEEE Conference on Acoustics, Speech, andSignal Processing
IEEE Transactions on Acoustics, Speech, and Signal Processing
HTK 2.1
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3
A Decision Theoretic Formulation for RobustAutomatic Speech Recognition
Qiang HuoThe University of Hong Kong, Hong Kong, China
CONTENTS
3.1 Introduction
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decision problem �
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statistical pattern recognition
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FIGURE 3.1Communication Theoretic View of ASR: Noisy Channel for Speech Generationand Signal Capturing (adapted from [68]).
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statistical decision
3.2 Optimal Bayes’ Decision Rule for ASR
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Bayes’ decision rule
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3.3 Adaptive Decision Rules Constructed from Training Samples
true ������true prior uncertainty
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3.3.1 Plug-in Bayes’ Decision Rules with Maximum-likelihood DensityEstimate
3.3.1.1 What are Plug-in Bayes’ Decision Rules?
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3.3.1.2 Why Could Plug-in Bayes’ Decision Rules Work?
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3.3.1.3 Implications on Parametric Models and Parameter Estimation
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Bayes’ risk consistent
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representative
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Discrete HMM Contin-uous Density HMM �
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conditional maximum likelihood estimate H-criteria
corrective training minimum empirical classification error
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3.3.2 Maximum-Discriminant Decision Rules Minimizing the Empiri-cal Classification Error
3.3.2.1 What are Maximum-Discriminant Decision Rules?
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3.3.2.2 Why Could Discriminant Approach Work?
Theorem: (Uniform Convergence) m-convex �
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3.3.2.3 Implications on the Choice of Discriminant Functions and the PracticalTraining Algorithms
3.3.3 Discussion
plug-in MAP
maximum discriminantminimum empirical classification error
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representative
3.4 Violations of Modeling Assumptions in ASR
3.4.1 Types of Distortions
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3.4.2 Towards Adaptive and Robust ASR
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3.5 Improving Adaptive Decision Rules via Decision ParameterAdaptation
3.5.1 Decision Parameter Adaptation for Stationary Operating Condi-tions
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3.5.1.1 Adaptation for Plug-in Decision Rules
Remark 1:regularization
imposing constraints
maximum penalized likelihood
Remark 2:
3.5.1.2 Adaptation for Maximum-Discriminant Decision Rules
w.r.t.
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forgetting mechanisms
3.5.3 Decision Parameter Adaptation for Switching Operating Condi-tions
adaptive model fu-sion
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3.5.4 Discussion
robustdecision rule
3.6 Robust Decision Rules
3.6.1 Decision Rule Robustness
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model-space stochastic matching
3.6.3 Bayesian Predictive Classification Rule
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point estimate ��� �� ������� �
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3.6.4 Discussion
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3.7 Summary
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Acknowledgement
References
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Statistical Prediction Analysis
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Pattern Classification and Scene Analysis
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Computer Speech and Language
Speech Com-munication
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Biometrika
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Proc. Eurospeech-01
RobustStatistics: The approach Based on Influence Functions
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Speech Communication
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Spoken language processing: aguide to theory, algorithm, and system development
Robust Statistics
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IEEE Trans. on Speechand Audio Processing
IEEE Trans. on Speech and Audio Processing
Speech Communication
Proc. ICSLP-2000
IEEE Trans. on Speech and Audio Processing
Proc. ICASSP-2000
IEEE Trans. on Pattern Analysis and Machine Intelligence
Proceed-ings of the IEEE
Statistical Method for Speech Recognition
Advances in Speech Signal Processing
IEEE Trans. on Speech andAudio Processing
IEEE Trans. on Speech and Audio Processing
SpeechCommunication
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IEEE Trans. on Speech and Audio Processing
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Technometrics
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Computer Speech andLanguage
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Computer Speech and Language
Proc. of ICASSP-2001
Proc. of ASRU-1999
Robustness in Statistical Pattern Recognition
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IEEESignal Processing Letters
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Speech Communication
Proceedings of the IEEE
The Bell System Technical Journal
Proc.IEEE
IEICE Trans. Inf. & Syst.
Automatic Speech Recognition – The Development of the SPHINX-System
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IEEE Trans. on Information Theory
Proc. ICASSP-90
Proc. Eurospeech-95
IEEE Trans. on Signal Processing
IEEE Trans. on Speech and Audio Process-ing
IEEE Trans. onNeural Networks
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IEEE Trans. on Acoustics, Speech, and Signal Processing
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Proceedings of the IEEE
Proceedings of the IEEE
IEEETrans. on Speech and Audio Processing
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AT&T Tech. Journal
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Proceedings of the IEEE
Fundamentals of Speech Recognition
Pattern Recognition and Neural Networks
Annals ofMathematical Statistics
IEEESignal Processing Letters
IEEE Trans. on Speech and AudioProcessing
IEEE Trans. on Speech and Audio Processing
Proc. Workshop on Adaptation Methods for SpeechRecognition
Proc. ETRW on Ro-bust Speech Recognition For Unknown Communication Channels
IEEE Trans. on Audio and Speech Processing
Speech Communication
IEICE Trans. Inf. & Syst.
Adaptation and learning in automatic systems
Foundations of the theory of learning systems
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Proc. ICASSP-01
Proc. of ICASSP-2001
Proc. ICASSP-00
Statistical Decision Functions
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Proc. ICASSP-2002
Proc. of Eurospeech-2001
The HTK Book Version 3.0
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4
Speech Pattern Recognition using NeuralNetworks
Shigeru KatagiriNTT Communication Science Laboratories
CONTENTS
4.1 Introduction
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4.2 Bayes Decision Theory
4.2.1 Preparations
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4.2.3 Minimum Error-rate Classification
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4.2.4 Probability Function Estimation
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4.2.5 Discriminative Training
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4.2.5.1 Functional Form Embodiment of the Entire Process
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4.2.5.2 Discriminant Functions
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4.2.5.3 Loss over an Individual Pattern
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4.2.5.4 Loss over Multiple Patterns
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4.2.5.7 Global Design Scope
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4.3 Speech Recognizers Based on Neural Networks
4.3.1 Preparations
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4.3.2 Classification Error Minimization
4.3.2.1 Learning Vector Quantization
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FIGURE 4.1Architecture of shift-tolerant LVQ classifier [20].
4.3.2.3 LVQ/HMM Hybrid Classifier
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FIGURE 4.2Block diagram of LVQ/HMM hybrid classifier.
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4.3.2.4 HMM/LVQ Hybrid Classifier
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FIGURE 4.3Block diagram of HMM/LVQ hybrid classifier.
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4.3.3 Squared Error Minimization
4.3.3.1 Training Using the Squared Error Loss
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FIGURE 4.4Architecture of time-delay neural network [27].
4.3.3.2 Time-delay Neural Network
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FIGURE 4.5Schematic description of distance classifier as a single intermediate layer net-work (2-dimensional input, 3 references/class, 3 classes).
4.3.3.3 Multi-state Time-delay Neural Network
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4.3.4 Cross Entropy Minimization
4.3.4.1 Training Using the Cross Entropy Loss
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FIGURE 4.6Architecture of unidirectional network [23].
4.4 Fusion of Multiple Classification Decisions
4.4.1 Principles
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FIGURE 4.7Architecture of bi-directional network [25].
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FIGURE 4.8Typical classifier design schemes of averaging-based decision fusion.
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4.4.2 Examples of Embodiment
4.4.2.1 Multi-codebook Classifier Designed with GPD
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FIGURE 4.9Relation between recognition accuracy and the number of prototypes per classand codebook [3].
4.4.2.2 Multi-class Classification Based on Support Vector Machine
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4.4.2.3 Decision Fusion Using Different Classifiers
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FIGURE 4.10Typical block diagrams of the MSTDNN-based audio-visual speech recognition[7].
4.4.2.4 Decision Fusion Using Multi-modal Classifiers
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FIGURE 4.11Block diagram of the twofold-HMM-based audio-visual speech recognition [21].
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4.5 Concluding Remarks
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References
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4.6 Appendix: Maximizing Mutual Information
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5
Large Vocabulary Speech Recognition Basedon Statistical Methods
Jean-Luc Gauvain and Lori LamelLIMSI, France
CONTENTS
5.1 Introduction
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5.2 Overview
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FIGURE 5.1LVCSR speech generation model: The word sequence � produced by the lan-guage model is successively transformed by the pronunciation model (� �� �� �)and the acoustic model (��� ���� �), resulting in the speech signal � .
5.3 Language Modeling
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FIGURE 5.2System diagram of a generic speech recognizer based on statistical models, in-cluding training and decoding processes and the main knowledge sources.
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5.3.1 Text Preparation
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FIGURE 5.3Some example transformation rules applied during text normalization with as-sociated probabilities.
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5.3.2 Vocabulary Selection
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5.3.3 N-gram Estimation
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5.4 Pronunciation Modeling
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Phone Example Phone Example
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excuse,record, moderate anti-, bi-, multi-, -ization
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5.5 Acoustic Modeling
5.5.1 Acoustic Front-end
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FIGURE 5.6A simple 3-state left-to-right HMM topology commonly used for allophone mod-eling in LVCSR. The model generates at least 3 speech frames per allophone, re-sulting in a minimal phone segment duration of 30ms for frame rate of 100Hz.
5.5.2 Modeling Allophones
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FIGURE 5.8Example questions used for decision tree clustering.
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5.5.3 HMM Parameter Estimation
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Question Log likelihood gain Question Log likelihood gain
FIGURE 5.9The most frequently used decision tree questions for an American Englishbroadcast news transcription system [40]. The [+1] and [-1] indicate that thequestion has been applied to the right or left context respectively, and [0] to thephone itself.
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A Posteriori
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5.6 Decoding
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5.6.1 Speech/Non-speech Detection
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5.6.2 Decoding Strategies
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FIGURE 5.10Example word lattice generated by a speech recognizer using a bigram languagemodel for a 2.1s utterance. Each graph edge corresponds to a word hypothesisand a time interval (as specified by the time information on the nodes). In thisexample the word transcription with the highest likelihood is “sil IT WAS AGOOD PROGRAM sil” which happens to be what was said. (The acoustic andlanguage model likelihoods are not given on the figure.)
5.6.3 Efficiency
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5.7.1 Dictation
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5.8 Portability and Language Dependencies
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References
The THISL Broadcast NewsRetrieval System,
Experiments in Vocal Tract Normaliza-tion,
A CompactModel for Speaker Adaptation Training,
One Pass Cross Word Decoding for Large Vocabularies Based on aLexical Tree Search Organization, 4
The Forward-Backward Search Strat-egy for Real-Time Speech Recognition,
Preliminary results on the performance of a system for the au-tomatic recognition of continuous speech,
AcousticMarkov Models used in the Tangora Speech Recognition System,
1
A Maximum Likelihood Approach toContinuous Speech Recognition,
PAMI-5
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A Fast Match for Continuous Speech Recognition Using Allophonic Models,1
Large Vocabulary Recogni-tion of Wall Street Journal Sentences at Dragon Systems,
A maximization technique oc-curring in the statistical analysis of probabilistic functions of Markov chains
41
Vector quantization for efficient computation of continuous den-sity likelihoods, 2
A Baseline for the Tran-scription of Italian Broadcast News,
Word and acoustic confidence annotation for large vocabularyspeech recognition
Improvements in Language, Lexical and PhoneticModeling in Sphinx-II,
An empirical study of smoothing techniques forlanguage modeling, 13
Speaker, Environment and ChannelChange Detection and Clustering via the Bayesian Information Criterion
The Role of Word-Dependent Coartic-ulatory Effects in a Phoneme-Based Speech Recognition System
3
Statistical Language Modelling using CMU-Cambridge Toolkit,
Comparison of Parametric Representations ofMonosyllabic Word Recognition in Continuously Spoken Sentences,
28
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Maximum Likelihood from In-complete Data via the EM Algorithm
39
Human SpeechRecognition Performance on the 1995 CSR Hub-3 Corpus
Genones: Optimization the Degree of Tying ina Large Vocabulary HMM-based Speech Recognizer,1
Speaker adaptation using con-strained estimation of Gaussian mixtures3
Sonograph and Sound Mechanics,22
Automatic Recognition of Phonetic Patterns inSpeech, 30
Human Speech Recognition Performance on the 1994CSR Spoke 10 Corpus
Comparison of speaker recognition methods using statistical featuresand dynamic features,ASSP-29
An improved approach to hidden Markov modeldecomposition of speech and noise,
Robust Continuous Speech Recognition usingParallel Model Combination, 9
Cluster Adaptive Training for Speech Recognition,
Semi-Tied Covariance Matrices for Hidden Markov Models,7
Transcribing Broad-cast News: The LIMSI Nov96 Hub4 System,
Spoken Lan-guage component of the MASK Kiosk
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Speech Recognition for an Informa-tion Kiosk,
Partitioning and Transcription of Broad-cast News Data, 5
Developments in ContinuousSpeech Dictation using the ARPA WSJ Task,
Maximum a Posteriori Estimation for Multivari-ate Gaussian Mixture Observations of Markov Chains,
2
The LIMSI Broadcast News TranscriptionSystem 37
A Rapid Match Algorithm for Continuous SpeechRecognition,
A Probabilistic Approach to Confidence Mea-sure Estimation and Evaluation
Real-time Telephone-basedSpeech Recognition in the Jupiter Domain, 1
SWITCHBOARD: Telephone SpeechCorpus for Research and Development,
The Population Frequencies of Species and the Estimation of Popu-lation Parameters 40
A tree search strategyfor large-vocabulary continuous speech recognition,1
Linear Discriminant Analysis for ImprovedLarge Vocabulary Continuous Speech Recognition, 1
SegmentGeneration and Clustering in the HTK Broadcast News Transcription System,
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News-on-Demand-’An Ap-plication of Informedia Technology’,
The ATIS Spoken LanguageSystems Pilot Corpus,
Perceptual linear predictive (PLP) analysis of speech,87
Large vocabu-lary continuous speech recognition using a hybrid connectionist-HMM system,
Signal Representation
Subphonetic Modeling with Markov States - Senone,1
Predicting Unseen Triphones withSenones, II
Continuous Speech Recognition by Statistical Methods,64
Statistical Methods for Speech Recognition,
A Dynamic LanguageModel for Speech Recognition,
: Speech BasedVideo Retrieval,
Maximum-Likelihood Estimation for Mixture MultivariateStochastic Observations of Markov Chains 64
Estimation of Probabilities from Sparse Data for the LanguageModel Component of a Speech Recognizer,
ASSP-35
Unsupervised Training of a Speech Recognizer: Re-cent Experiments, 6
The 1995 Abbot hybridconnectionist-HMM large-vocabulary recognition system,
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Improved Clustering Techniques for Class-Based Statis-tical Language Modelling,
Improved backing-off for n-gram language modeling,1
Design of the 1994 CSR Benchmark Tests,
Toward Automatic Recognition of Broadcast News,
Heteroscedastic discriminant analysis and re-duced rank HMMs for improved speech recognition,26
Eigenvoices for Speaker Adaptation,
On Designing Pronunciation Lexicons for Large Vo-cabulary, Continuous Speech Recognition, 1
Speech Recognition of European Languages,
Continuous Speech Recognition at LIMSI,
A Phone-based Approach to Non-LinguisticSpeech Feature Identification, 9
Lightly Supervised and UnsupervisedAcoustic Model Training 16
Development of Spoken Language Corpora for Travel Infor-mation 3
Large-vocabulary speaker-independent continuous speech recogni-tion: The SPHINX system,
Speaker Normalization Using Efficient Frequency Warp-ing Procedures 1
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Maximum Likelihood Linear Regression forSpeaker Adaptation of Continuous Density Hidden Markov Models,
9
Maximum Likelihood Estimation for Multivariate Observa-tions of Markov Sources IT-28
Speech recognition by machines and humans,22
Fast Speaker Change Detection for Broadcast NewsTranscription and Indexing 3
Multi-site Data Collection for a Spoken Language Corpus,
Finding Consensus in Speech Recognition:Word Error Minimization and Other Applications of Confusion Networks,
Subspace distribution clustering for continuousobservation density hidden Markov models,
Spoken Language Processing and Human-Machine Communica-tion in the European Union Programs,
An overview of EU programs related to conver-sational/interactive systems,
Algorithms for Bigram and Trigram Clus-tering,
News on Demand,43
Named Entity Extrac-tion from Broadcast News,
Full Expansion ofContext-Dependent Networks in Large Vocabulary Speech Recognition,
Large-VocabularyDictation using SRI’s Decipher Speech Recognition System: Progressive
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Search Techniques, II
The Use of a One-Stage Dynamic Programming Algorithm for Con-nected Word Recognition,
ASSP-32
Improvements in BeamSearch for 10000-Word Continuous Speech Recognition,
I
Single-Tree Method for Grammar-DirectedSearch, 2
The Use of Decision Trees with Context Sensitive Phoneme Mod-elling,
A One Pass DecoderDesign for Large Vocabulary Recognition,
Recent Advancesin Japanese Broadcast News Transcription,2
Modeling Inverse Covariance Matrices by Ba-sis Expansion,
Language-model look-ahead for largevocabulary speech recognition,
A Word Graph Algorithm for Large Vo-cabulary Continuous Speech Recognition,11
The Role ofPhonological Rules in Speech Understanding Research,
ASSP-23
Continuous WordRecognition Based on the Stochastic Segment Model,
1993 Benchmark Tests for the ARPA Spoken Language Program,
1994 Benchmark Tests for the ARPA Spoken Language
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Program,
1995 Hub-3 Multiple Microphone Corpus Benchmark Tests,
1998Broadcast News Benchmark Test Results: English and Non-English Word Er-ror Rate Performance Measures,
An efficient A� stack decoder algorithm for continuous speechrecognition with a stochastic language model,
Improved Discriminative Training Techniques ForLarge Vocabulary Continuous Speech Recognition
Evaluation of Spoken Language Systems: The ATIS Domain,
An Introduction to Hidden Markov ModelsASSP-3
Efficient Algorithms for Speech Recognition,
Stochastic pronuncia-tion modelling from hand-labelled phonetic corpora,29
Improvements in Stochastic Language Modeling,
Adaptive Statistical Language Modeling,
Two Decades of Statistical Language Modeling: Where Do WeGo From Here?,
88
Language-independent and langauge-adaptiveacoustic modeling for speech recognition 35
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Memory-efficient LVCSR search using a one-pass stack decoder,14
New uses for N-Best Sen-tence Hypothesis, within the BYBLOS Speech Recognition System,
I
Improved Hid-den Markov Modeling of Phonemes for Continuous Speech Recognition,
3
NYU Language Modeling Experiments for the1995 CSR Evaluation,
A Markov Random Field Approach to Bayesian SpeakerAdaptation,
Modeling Those F-Conditions – Or Not,
Scalable backoff language models1
Automatic Segmentation, Classifica-tion and Clustering of Broadcast News Audio,
Evaluation of word confidence for speech recognitionsystems 13
Entropy-based Pruning of Backoff Language Models
Four-level Tied Structure for Efficient Repre-sentation of Acoustic Modeling,
An Investigation into Vocal Tract LengthNormalization,
Human Bench-marks for Speaker Independent Large Vocabulary Recognition Performance,
Speech discrimination by dynamic programming,4
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Elements-wise recognition of continuous speech composed ofwords from a specified dictionary, 7
Verbmobil: Translation of Face-to-Face Dialogs,Plenary
Multilinguality in Speech and Spoken Language Systems88
Probabilistic Models for Topic De-tection and Tracking, 1
DragonSystems’ 1997 Broadcast News Transcription System,
Progress in Broadcast News Transcrip-tion at Dragon Systems,
Neural-Network based Measures of Confidence for Word Recognition,
Using word probabilities as confi-dence measures,
Unsupervised training of acoustic models for large vo-cabulary continuous speech recognition
The Zero Frequency problem: Estimating the prob-lems of Novel Events in Adaptive tex Compression
37
Large scale discriminative training of hiddenMarkov models for speech recognition,16
The de-velopment of the 1994 HTK large vocabulary speech recognition system,
The HTK large vocab-ulary recognition system for the 1995 ARPA H3 task,
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A Hid-den Markov Approach to Text Segmentation and Event Tracking
1
A Review of Large-Vocabulary Continuous Speech Recognition,13
Multilingual large vocabulary speech recognition: the Euro-pean SQALE project, 11
Speech recognition evaluation: a review of the U.S.CSR and LVCSR programmes, 12
Tree-Based State Tying for High Ac-curacy Acoustic Modeling,
The Use of State Tying in Continuous SpeechRecognition, 3
Utilizing Untranscribed Training Data to Im-prove Performance
Maximum a Posteriori Adap-tation for Large Scale HMM Recognizers,
The MIT Speech Recog-nition System: A Progress Report
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6
Toward Spontaneous Speech Recognition andUnderstanding
Sadaoki FuruiTokyo Institute of Technology
CONTENTS
6.1 Introduction
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FIGURE 6.1Progress of spoken language technology along the dimensions of vocabulary sizeand speaking styles.
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6.2 Four Categories of Speech Recognition Tasks
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TABLE 6.1
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6.3 Spontaneous Speech Recognition and Understanding - Re-view
6.3.1 Category I (human-to-human dialogue)
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6.3.2 Category II (human-to-human monologue)
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FIGURE 6.2The SCANMail architecture [12].
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6.3.3 Category III (human-to-machine dialogue)
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FIGURE 6.3AT&T Communicator architecture [15].
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6.4 Japanese National Project on Spontaneous Speech Corpusand Processing Technology
6.4.1 Project Overview
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6.4.2 Corpus
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FIGURE 6.5Overall design of the Corpus of Spontaneous Japanese.
6.5 Automatic Transcription of Spontaneous Presentation
6.5.1 Recognition Task
6.5.2 Language and Acoustic Modeling
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TABLE 6.2
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6.5.3 Recognition Results
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FIGURE 6.6Test-set perplexity and OOV rate for the two language models.
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FIGURE 6.7Word accuracy for each combination of models.
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6.5.4 Analysis on Individual Differences
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FIGURE 6.8Results of unsupervised adaptation.
6.5.4.1 Speaker Attributes
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TABLE 6.3
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6.5.4.2 Correlation Analysis
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6.5.4.3 Regression Analysis
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FIGURE 6.9Speaking rate vs. word accuracy.
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FIGURE 6.10Summary of correlation between various attributes.
TABLE 6.4
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6.5.5 Discussion
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6.6 Automatic Speech Summarization and Evaluation
6.6.1 Summarization of Each Sentence Utterance
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FIGURE 6.11An example of dependency structure.
6.6.1.2 Linguistic Score
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6.6.3 Evaluation
6.6.3.1 Word Network of Manual Summarization Results for Evaluation
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6.6.3.2 Evaluation Data
6.6.3.3 Training Data for Summarization Models
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6.6.3.4 Evaluation Results
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6.6.4 Discussion
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6.7 Spontaneous Speech Recognition and Understanding ResearchIssues
6.7.1 Language Models and Corpora
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6.7.2 Message-driven Speech Recognition and Understanding
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6.7.3 Statistical Approaches and Speech Science
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6.7.5 Dynamic Spectral Features
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FIGURE 6.16Speech-generation and speech-perception processes.
6.8 Conclusion
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References
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7
Speaker Authentication
Qi Li� and Biing-Hwang Juang�
�Bell Labs; �Avaya Labs Research
CONTENTS
7.1 Introduction
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FIGURE 7.1Speaker authentication approaches.
Speaker authentication
7.1.1 Speaker Recognition and Verification
Speaker recognitionSpeaker verification
hypothesis test-ing Speaker identification
classification
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FIGURE 7.2A speaker verification system.
direct methoddirectly
fixed pass-phrase system
text-prompted system
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text-independentSV system
closed test open test
7.1.2 Verbal Information Verification
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FIGURE 7.3An example of verbal information verification by asking sequential questions.(Similar sequential tests can also be applied in speaker verification and otherbiometric or multi-modality verification.)
in-direct method
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7.2 Pattern Recognition in Speaker Authentication
7.2.1 Bayesian Decision Theory
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7.2.3 Stochastic Models for Non-Stationary Process
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FIGURE 7.4Left-to-right hidden Markov model.
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7.2.4 Speech Segmentation
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TABLE 7.1Experimental Results in Average Equal-Error Rates
7.4 Verbal Information Verification
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FIGURE 7.6Utterance verification in verbal information verification (VIV).
7.4.1 Utterance Segmentation
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7.4.4 Sequential Utterance Verification
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FIGURE 7.7An integrated voice authentication system combining verbal information verifi-cation and speaker verification.
7.5 Speaker Authentication by Combining SV and VIV
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TABLE 7.3Experimental Results without Adaptation in Average Equal-Error Rates
TABLE 7.4Experimental Results with Adaptation in Average Equal-Error Rates
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7.6 Summary
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References
An Introduction to Multivariate Statistical Analysis
Journal of the AcousticalSociety of America
Proceedingof the IEEE
Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing
Ann. Math. Stat.
Proceedings of the IEEE
Proceedings of the IEEE International Conferenceon Acoustics, Speech, and Signal Processing
Journal of Royal Statistical Society
Pattern Classification, Second Edition
Proceeding of IEEE
Introduction to Statistical Pattern Recognition
IEEETrans. Acoust., Speech, Signal Processing
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IEEE SignalProcessing Magazine
AT&T Technical Journal
IEEE Trans. on Speech and Audio Process.
IEEE Transactions on Signal Processing
Proceed-ings of ICASSP
Proceedings of Int. Conf. on Spoken Language Processing
Proc.of ICSLP
IEEE Trans. on Speech and Audio Processing
Proceedings of the IEEE InternationalConference on Acoustics, Speech, and Signal Processing
IEEE Robotics & Automation magazine
Proceedings of EUROSPEECH
IEEE Trans. on Speech and Audio Processing
Proceedings of theIEEE International Conference on Acoustics, Speech, and Signal Processing
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IEEE International Conference on Acoustics, Speech, and Signal Processing
Proceedings of IEEE Workshop on Automatic Identifi-cation
IEEE Trans. onSpeech and Audio Processing
Journal of theAcoustical Society of America
Proc. IEEE Int. Conf.Acoust., Speech, Signal Processing
Biometrika
Phil. Trans. Roy. Soc. A
IEEE Trans. onSpeech and Audio Processing
Proceedingsof ICSLP-96
Fundamentals of speech recognition
AT&T Technical Journal
Proc. IEEE Int. Conf. Acoust., Speech, SignalProcessing
Proc. IEEE Int. Conf. Acoustic, Speech, Signal Processing
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IEEE Trans. on Speech and Audio Processing
Proceedings of theIEEE
Proceedings of the IEEE In-ternational Conference on Acoustics, Speech, and Signal Processing
Proc. IEEE Int. Conf. Acoust., Speech,Signal Processing
Proc. Int. Conf. on Spoken LanguageProcessing
Proc. IEEE Int. Conf. Acoust., Speech,Signal Processing
AT&T Technical Journal
IEEE Trans. Speech and Audio Process.
Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing
IEEE Transactions on Information Theory
Sequential analysis
IEEETrans. on Acoustics, Speech, and Signal Proc.
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The Annalsof Statistics
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8
HMMs for Language Processing Problems
Richard M. Schwartz and John MakhoulBBN Technologies, Verizon
CONTENTS
8.1 Introduction
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8.2 Use of Probabilities
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8.2.1 Hidden Markov Models
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8.3 Name Spotting
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8.4 Topic Classification
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8.4.1 The Model
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8.4.3 Classification
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8.5 Information Retrieval
8.5.1 A Bayesian Model for IR
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8.5.2 Training the IR HMM
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8.6 Event Tracking
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8.7 Unsupervised Topic Detection
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8.8 Summary
References
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9
Statistical Language Models With EmbeddedLatent Semantic Knowledge
Jerome R. BellegardaApple Computer, Inc.
CONTENTS
9.1 Introduction
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9.1.3 Semantically–Driven Span Extension
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FIGURE 9.1Improved Topic Separability in LSA Space.
9.3 LSA Feature Space
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9.3.3 Document Clustering
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FIGURE 9.3Document Cluster Example.
9.3.4 Document Cluster Example
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9.4 Semantic Classification
9.4.1 Framework Extension
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FIGURE 9.4An Example of Semantic Inference for Command and Control (� � �).
9.4.3 Caveats
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9.6 Smoothing
9.6.1 Word Smoothing
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9.6.2 Document Smoothing
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9.7.1 Experimental Conditions
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TABLE 9.1
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TABLE 9.2
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TABLE 9.3
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References
Context-Dependent Vector Clustering for Speech Recognition
A Multi-Span Language Modeling Framework for Large Vo-cabulary Speech Recognition
Large Vocabulary Speech Recognition With Multi-Span Sta-tistical Language Models
Exploiting Latent Semantic Information in Statistical Lan-guage Modeling
Robustness in Statistical Language Modeling: Review andPerspectives
Fast Update of Latent Semantic Spaces Using a Linear Trans-form Framework
ANovel Word Clustering Algorithm Based on Latent Semantic Analysis
Toward Unconstrained Command andControl: Data-Driven Semantic Inference
Natural Language Spoken InterfaceControl Using Data-Driven Semantic Inference
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Large–Scale Sparse Singular Value Computations
Using Linear Algebra for In-telligent Information retrieval
An Overview of Parallel Algorithms for the SingularValue and Dense Symmetric Eigenvalue Problems
Natural Language Call Routing: A Robust,Self–Organized Approach
Structure and Perfor-mance of a Dependency Language Model
Recognition Performance of a Structured LanguageModel
Building Probabilistic Models for Natural Language
Dialog Management in Vector–Based CallRouting
Language Model Adaptation Using Mix-tures and an Exponentially Decaying Cache
Towards Better Integration of Semantic Predictorsin Statistical Language Modeling
Lanczos Algorithms for Large SymmetricEigenvalue Computations – Vol. 1 Theory
Recognizing and Using Knowledge Structures in Dialog Sys-tems
Indexing by Latent Semantic Analysis
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Adaptive Lan-guage Model Estimation Using Minimum Discrimination Estimation
Improving the Retrieval of Information from External Sources
Latent Semantic Indexing (LSI) and TREC–2
Language Modeling
Personalized Information Delivery: An Analysisof Information Filtering Methods
On Topic Identification and Dialogue Move Recognition
Topic–Based Language Modeling Using EM
Matrix Computations
Document Space Models Using Latent Semantic Anal-ysis
Probabilistic Latent Semantic Analysis
Probabilistic Topic Maps: Navigating Through Large Text Col-lections
Modeling Long Distance Dependencies in Lan-guage: Topic Mixtures Versus Dynamic Cache Models
Self–Organized Language Modeling for Speech Recognition
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Putting Language into Language Modeling
Using a Stochastic Context–Free Grammar as a Language Model forSpeech Recognition
Putting Language Back into Language Modeling
Statistical Language Modeling Using a Variable Context
The Hub and Spoke Paradigm for CSR Evaluation
A Cache-based Natural Language Method for SpeechRecognition
Cluster Expansion and Iterative Scaling for Maxi-mum Entropy Language Models
Solution to Plato’s Problem: The LatentSemantic Analysis Theory of Acquisition, Induction, and Representation ofKnowledge
How Well Can Pas-sage Meaning Be Derived Without Using Word Order: A Comparison of LatentSemantic Analysis and Humans
Trigger–Based Language Models: AMaximum Entropy Approach
On Structuring Probabilistic Dependencesin Stochastic Language Modeling
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A Variable–Length Category–Based N–Gram Lan-guage Model
Latent Seman-tic Indexing: A Probabilistic Analysis
Beyond Word �-Grams
An Overview of Automatic SpeechRecognition
The CMU Statistical Language Modeling Toolkit and its Use inthe 1994 ARPA CSR Evaluation
A Maximum Entropy Approach to Adaptive Statistical LanguageModeling
Two Decades of Statistical Language Modeling: Where Do WeGo From Here
Interactive Feature Induc-tion and Logistic Regression for Whole Sentence Exponential Language Mod-els
Language Representation
A MaximumLikelihood Model for Topic Classification of Broadcast News
An Explanation of the Effectiveness of Latent Semantic Indexing byMeans of a Bayesian Regression Model
Combining Nonlocal, Syntactic and N-Gram De-pendencies in Language Modeling
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Recognition and Parsing of Context–Free Languages in Time�
�
Using Detailed Linguistic Structure inLanguage Modeling
Linguistic Features for Whole Sen-tence Maximum Entropy Language Models
Integration of Speech Recognition and Natural Language Processing in theMIT Voyager System
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10
Semantic Information Processing of SpokenLanguage – How May I Help You?sm
A. L. Gorin, A. Abella, T. Alonso, G. Riccardi, and J. H. Wright,AT&T Laboratories
CONTENTS
10.1 Introduction
AT&T’s‘How May I Help You?’ ��
“The fundamental problem of communication is that of reproducing atone point either exactly or approximately a message selected at another
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point. Frequently the messages have meaning, � � � These semantic as-pects of communication are irrelevant to the engineering problem.”
confirmclarify
“Do you want to make a collect call?”“Charge this call please”
“How do you want to charge this call, to a credit card or to a third num-ber?”
“What is your home phonenumber?”
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Construct Algebra
dialog motivators
inheritance hierarchy
‘is a’‘has a’
10.2 Call-Classification
‘press one if you want x, press two if you want y’
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‘please say collect, calling card’ ‘press orsay one if you want x’
‘How may I help you?’
“I want to reverse the charges on this call.”“Can you tell me what time it is in Tokyo?”“I was trying to call my sister and dialed a wrong number.”“I’ve been trying to dial this number all day and can’t get through.”
“How much money do I owe you?”“I don’t recognize this phone call to Tallahassee on October 4.”“What’s this charge for one dollar and fifty cents?”“I have a question about my bill.”
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FIGURE 10.1Call classification and routing in HMIHY.
‘How may I help you?’
‘How may I help you?’
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FIGURE 10.2Inheritance hierarchy of task knowledge in operator services.
perplexity
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FIGURE 10.3Histogram of utterance lengths.
false rejection
correct classificationtrue rejection rate
Remark:
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“I want to know howto pay my bill”
10.3 Language Modeling for Recognition and Understanding
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FIGURE 10.4A salient grammar fragment.
salient grammar fragments
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FIGURE 10.5Natural spoken dialog in HMIHY.
10.4 Dialog
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Machine:User:Machine:User:Machine:User:Machine:User:Machine:
Machine:User:Machine:User:Machine:
User:Machine:
10.5 Conclusions
www.research.att.com/�algor/hmihy
References
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11
Machine Translation Using StatisticalModeling
Herman Ney, and F. J. OchAachen University of Technology, Germany
CONTENTS
Abstract.
11.1 Introduction
machine translationwritten language text
spoken speech
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spontaneous speech
� Statistical Decision Theory and Linguistics.
� Alignment and Lexicon Models.
� Alignment Templates: From Single Word to Word Groups.
� Experimental Resultswritten spoken
� Speech Translation: The Integrated Approach.serial
integrated
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11.2 Statistical Decision Theory and Linguistics
11.2.1 The Statistical Approach
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11.2.2 Bayes Decision Rule for Written Language Translation
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Source Language Text
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FIGURE 11.1Architecture of the translation approach based on Bayes decision rule.
11.3 Alignment and Lexicon Models
11.3.1 Concept of Alignment Modelling
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FIGURE 11.2Example of an alignment for a German-English sentence pair.
exactlyonealignment models
11.3.2 Hidden Markov Models
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SENTENCE INSOURCE LANGUAGE
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FIGURE 11.4Illustration of bottom-to-top search.
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11.4.1 Concept
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11.5 Experimental Results
11.5.1 The Task and the Corpus
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TABLE 11.2
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11.6.1 Principle
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Acknowledgment
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11.8 References
Spoken Language Translation Workshop, 35th Annual Conf. of the As-soc. for Computational Linguistics
Computational Linguistics
Int. Conf. on Spoken Language Processing,
ARPA Human Language Technology Workshop
United States Patent
Computational Linguistics
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Computer Speech and Language
ComputationalLinguistics
� Computational Linguistics
IEEE Automatic Speech Recognition and Understanding Workshop
Words and objections. Essays on the work of W. V. Quine
Workshop on Very Large Corpora
Int. Conf. on Spoken Language Processing,
Final report of the EuTrans project
39thAnnual Meeting of the Assoc. for Computational Linguistics,
39th Annual Meeting of the Assoc.for Computational Linguistics,
Statistical methods for speech recognition.
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Europ. Conf. on Speech Communication and Technology,
Computational Linguistics
2nd Conf.of the Assoc. for Machine Translation in the Americas
IEEE Int.Conf. on Acoustics, Speech and Signal Processing,
IEEE Trans. on Speech and AudioProcessing
18th Int. Conf. on Computational Linguistics
2nd Int. Conf. on Language Resourcesand Evaluation
36th Annual Meeting of the Assoc. for Compu-tational Linguistics and 17th Int. Conf. on Computational Linguistics
9th Conf.of the Europ. Chapter of the Assoc. for Computational Linguistics
18th Int. Conf. on Computational Linguistics
38th Annual Meet-ing of the Assoc. for Computational Linguistics
Joint SIGDAT Conf. on Empirical Methods in Natural Lan-guage Processing and Very Large Corpora
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Data-Driven Machine Translation Workshop, 39thAnnual Meeting of the Assoc. for Computational Linguistics
IBM Research Report
Fundamentals of speech recognition
Data-Driven MachineTranslation Workshop, 39th Annual Meeting of the Assoc. for ComputationalLinguistics
6th Int. Workshop on Parsing Technologies
Data-Driven Machine Translation Workshop, 39th AnnualMeeting of the Assoc. for Computational Linguistics
18th Int. Conf. on Computational Linguistics 2000
IEEE Int. Conf. onAcoustics, Speech and Signal Processing
38th An-nual Meeting of the Assoc. for Computational Linguistics
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16th Int. Conf. on Computational Linguistics
Verbmobil: Foundations of speech-to-speech translation.
35th An-nual Conf. of the Assoc. for Computational Linguistics
IEEE Trans. on Speech and AudioProcessing,
Computational Linguistics
39thAnnual Meeting of the Assoc. for Computational Linguistics,
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12
Modeling Topics for Detection and Tracking
James AllanUniversity of Massachusetts Amherst
CONTENTS
12.1 Topic Detection and Tracking
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12.1.1 Topic and Events
event
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12.1.2 TDT Tasks
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12.1.2.1 Segmentation
12.1.2.2 Cluster Detection
12.1.2.3 Tracking
12.1.2.4 New Event Detection
12.1.2.5 Link Detection
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12.1.3 Corpora
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12.2 Basic Topic Models
12.2.1 Vector Space
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12.2.2 Language Models
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12.3.1 Named Entities
President Bush George Bush
12.3.2 Document Expansion
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12.3.3 Clustering
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12.3.4 Time Decay
12.4 Comparing Models
12.4.1 Nearest Neighbors
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12.4.2 Decision Trees
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12.4.3 Model-to-Model
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12.5 Miscellaneous Issues
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12.5.1 Deferral
12.5.2 Multi-modal Issues
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12.5.3 Multi-lingual Issues
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FIGURE 12.2Screen snapshot of the Lighthouse system that was created to portray TDT topicclusters and their relationships.
12.6 Using TDT Interactively
12.6.1 Demonstrations
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12.6.2 Timelines
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OklahomaMcVeigh Simpson
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FIGURE 12.3Overview of January-June 1998. The topic labeled monica lewinsky allegation isthe highest ranked topic by the �� measure. The pop-up on oregon school shoot-ing shows significant named entities for that event. The other pop-up displays asub-menu for obtaining more information on the name kip kinkel.
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12.7 Modeling Events
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12.8 Conclusion
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