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007 cr |n|||||||||
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010 _z 2008038551 (print)
020 _a9780470742044
_qelectronic
020 _z9780470696835
_qcloth
020 _z0470696834
_qcloth
024 7 _a10.1002/9780470742044
_2doi
035 _a(CaBNVSL)mat08040180
035 _a(IDAMS)0b00006485f0e68e
040 _aCaBNVSL
_beng
_erda
_cCaBNVSL
_dCaBNVSL
050 4 _aTK7895.S65
_bA983 2009eb
082 0 0 _a006.4/54
_222
245 0 0 _aAutomatic speech and speaker recognition :
_blarge margin and kernel methods /
_c[edited by] Joseph Keshet, Samy Bengio.
264 1 _aChichester, U.K. ;
_bJ. Wiley & Sons,
_c2009.
264 2 _a[Piscataqay, New Jersey] :
_bIEEE Xplore,
_c[2009]
300 _a1 PDF (xiii, 253 pages) :
_billustrations, plans.
336 _atext
_2rdacontent
337 _aelectronic
_2isbdmedia
338 _aonline resource
_2rdacarrier
504 _aIncludes bibliographical references and index.
505 0 _aList of Contributors -- Preface -- I Foundations -- 1 Introduction (Samy Bengio and Joseph Keshet) -- 1.1 The Traditional Approach to Speech Processing -- 1.2 Potential Problems of the Probabilistic Approach -- 1.3 Support Vector Machines for Binary Classification -- 1.4 Outline -- References -- 2 Theory and Practice of Support Vector Machines Optimization (Shai Shalev-Shwartz and Nathan Srebo) -- 2.1 Introduction -- 2.2 SVM and L2-regularized Linear Prediction -- 2.3 Optimization Accuracy From a Machine Learning Perspective -- 2.4 Stochastic Gradient Descent -- 2.5 Dual Decomposition Methods -- 2.6 Summary -- References -- 3 From Binary Classification to Categorial Prediction (Koby Crammer) -- 3.1 Multi-category Problems -- 3.2 Hypothesis Class -- 3.3 Loss Functions -- 3.4 Hinge Loss Functions -- 3.5 A Generalized Perceptron Algorithm -- 3.6 A Generalized Passive / Aggressive Algorithm -- 3.7 A Batch Formulation -- 3.8 Concluding Remarks -- 3.9 Appendix. Derivations of the Duals of the Passive / Aggressive Algorithm and the Batch Formulation -- References -- II Acoustic Modeling -- 4 A Large Margin Algorithm for Forced Alignment (Joseph Keshet, Shai Shalev-Shwartz, Yoram Singer and Dan Chazan) -- 4.1 Introduction -- 4.2 Problem Setting -- 4.3 Cost and Risk -- 4.4 A Large Margin Approach for Forced Alignment -- 4.5 An Iterative Algorithm -- 4.6 Efficient Evaluation of the Alignment Function -- 4.7 Base Alignment Functions -- 4.8 Experimental Results -- 4.9 Discussion -- References -- 5 A Kernel Wrapper for Phoneme Sequence Recognition (Joseph Keshet and Dan Chazan) -- 5.1 Introduction -- 5.2 Problem Setting -- 5.3 Frame-based Phoneme Classifier -- 5.4 Kernel-based Iterative Algorithm for Phoneme Recognition -- 5.5 Nonlinear Feature Functions -- 5.6 Preliminary Experimental Results -- 5.7 Discussion: Canwe Hope for Better Results? -- References -- 6 Augmented Statistical Models: Using Dynamic Kernels for Acoustic Models (Mark J. F. Gales) -- 6.1 Introduction -- 6.2 Temporal Correlation Modeling.
505 8 _a6.3 Dynamic Kernels -- 6.4 Augmented Statistical Models -- 6.5 Experimental Results -- 6.6 Conclusions -- Acknowledgements -- References -- 7 Large Margin Training of Continuous Density Hidden Markov Models (Fei Sha and Lawrence K. Saul) -- 7.1 Introduction -- 7.2 Background -- 7.3 Large Margin Training -- 7.4 Experimental Results -- 7.5 Conclusion -- References -- III Language Modeling -- 8 A Survey of Discriminative Language Modeling Approaches for Large Vocabulary Continuous Speech Recognition (Brian Roark) -- 8.1 Introduction -- 8.2 General Framework -- 8.3 Further Developments -- 8.4 Summary and Discussion -- References -- 9 Large Margin Methods for Part-of-Speech Tagging (Yasemin Altun) -- 9.1 Introduction -- 9.2 Modeling Sequence Labeling -- 9.3 Sequence Boosting -- 9.4 Hidden Markov Support Vector Machines -- 9.5 Experiments -- 9.6 Discussion -- References -- 10 A Proposal for a Kernel Based Algorithm for Large Vocabulary Continuous Speech Recognition (Joseph Keshet) -- 10.1 Introduction -- 10.2 Segment Models and Hidden Markov Models -- 10.3 Kernel Based Model -- 10.4 Large Margin Training -- 10.5 Implementation Details -- 10.6 Discussion -- Acknowledgements -- References -- IV Applications -- 11 Discriminative Keyword Spotting (David Grangier, Joseph Keshet and Samy Bengio) -- 11.1 Introduction -- 11.2 Previous Work -- 11.3 Discriminative Keyword Spotting -- 11.4 Experiments and Results -- 11.5 Conclusions -- Acknowledgements -- References -- 12 Kernel-based Text-independent Speaker Verification (Johnny Mari�ethoz, Samy Bengio and Yves Grandvalet) -- 12.1 Introduction -- 12.2 Generative Approaches -- 12.3 Discriminative Approaches -- 12.4 Benchmarking Methodology -- 12.5 Kernels for Speaker Verification -- 12.6 Parameter Sharing -- 12.7 Is the Margin Useful for This Problem? -- 12.8 Comparing all Methods -- 12.9 Conclusion -- References -- 13 Spectral Clustering for Speech Separation (Francis R. Bach and Michael I. Jordan) -- 13.1 Introduction -- 13.2 Spectral Clustering and Normalized Cuts.
505 8 _a13.3 Cost Functions for Learning the Similarity Matrix -- 13.4 Algorithms for Learning the Similarity Matrix -- 13.5 Speech Separation as Spectrogram Segmentation -- 13.6 Spectral Clustering for Speech Separation -- 13.7 Conclusions -- References -- Index.
506 _aRestricted to subscribers or individual electronic text purchasers.
520 _aThis book discusses large margin and kernel methods for speech and speaker recognition Speech and Speaker Recognition: Large Margin and Kernel Methods is a collation of research in the recent advances in large margin and kernel methods, as applied to the field of speech and speaker recognition. It presents theoretical and practical foundations of these methods, from support vector machines to large margin methods for structured learning. It also provides examples of large margin based acoustic modelling for continuous speech recognizers, where the grounds for practical large margin sequence learning are set. Large margin methods for discriminative language modelling and text independent speaker verification are also addressed in this book. Key Features: . Provides an up-to-date snapshot of the current state of research in this field . Covers important aspects of extending the binary support vector machine to speech and speaker recognition applications . Discusses large margin and kernel method algorithms for sequence prediction required for acoustic modeling . Reviews past and present work on discriminative training of language models, and describes different large margin algorithms for the application of part-of-speech tagging . Surveys recent work on the use of kernel approaches to text-independent speaker verification, and introduces the main concepts and algorithms . Surveys recent work on kernel approaches to learning a similarity matrix from data This book will be of interest to researchers, practitioners, engineers, and scientists in speech processing and machine learning fields.
530 _aAlso available in print.
538 _aMode of access: World Wide Web
588 _aDescription based on PDF viewed 10/24/2017.
650 0 _aAutomatic speech recognition.
_95558
655 0 _aElectronic books.
_93294
700 1 _aKeshet, Joseph.
_930861
700 1 _aBengio, Samy.
_930862
710 2 _aIEEE Xplore (Online Service),
_edistributor.
_930863
710 2 _aWiley,
_epublisher.
_930864
776 0 8 _iPrint version:
_z9780470696835
856 4 2 _3Abstract with links to resource
_uhttps://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=8040180
942 _cEBK
999 _c74928
_d74928