{"product_id":"automatic-speech-and-speaker-recognition-9780470696835","title":"Automatic Speech and Speaker Recognition","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eThis book discusses large margin and kernel methods for speech and speaker recognition\u003c\/b\u003e  \u003cp\u003e\u003ci\u003eSpeech and Speaker Recognition: Large Margin and Kernel Methods\u003c\/i\u003e 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.\u003c\/p\u003e \u003cp\u003eKey Features:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eProvides an up-to-date snapshot of the current state of research in this field\u003c\/li\u003e \u003cli\u003eCovers important aspects of extending the binary support vector machine to speech and speaker recognition appl\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eList of Contributors.\u003c\/b\u003e  \u003cp\u003e\u003cb\u003ePreface.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eI Foundations.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction\u003c\/b\u003e (\u003ci\u003eSamy Bengio and Joseph Keshet\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e1.1 The Traditional Approach to Speech Processing.\u003c\/p\u003e \u003cp\u003e1.2 Potential Problems of the Probabilistic Approach.\u003c\/p\u003e \u003cp\u003e1.3 Support Vector Machines for Binary Classification.\u003c\/p\u003e \u003cp\u003e1.4 Outline.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Theory and Practice of Support Vector Machines Optimization\u003c\/b\u003e (\u003ci\u003eShai Shalev-Shwartz and Nathan Srebo\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e2.1 Introduction.\u003c\/p\u003e \u003cp\u003e2.2 SVM and \u003ci\u003eL\u003c\/i\u003e\u003csub\u003e2\u003c\/sub\u003e-regularized Linear Prediction.\u003c\/p\u003e \u003cp\u003e2.3 Optimization Accuracy From a Machine Learning Perspective.\u003c\/p\u003e \u003cp\u003e2.4 Stochastic Gradient Descent.\u003c\/p\u003e \u003cp\u003e2.5 Dual Decomposition Methods.\u003c\/p\u003e \u003cp\u003e2.6 Summary.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 From Binary Classification to Categorial Prediction\u003c\/b\u003e (\u003ci\u003eKoby Crammer\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e3.1 Multi-category Problems.\u003c\/p\u003e \u003cp\u003e3.2 Hypothesis Class.\u003c\/p\u003e \u003cp\u003e3.3 Loss Functions.\u003c\/p\u003e \u003cp\u003e3.4 Hinge Loss Functions.\u003c\/p\u003e \u003cp\u003e3.5 A Generalized Perceptron Algorithm.\u003c\/p\u003e \u003cp\u003e3.6 A Generalized Passive–Aggressive Algorithm.\u003c\/p\u003e \u003cp\u003e3.7 A Batch Formulation.\u003c\/p\u003e \u003cp\u003e3.8 Concluding Remarks.\u003c\/p\u003e \u003cp\u003e3.9 Appendix. Derivations of the Duals of the Passive–Aggressive Algorithm and the Batch Formulation.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eII Acoustic Modeling.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 A Large Margin Algorithm for Forced Alignment\u003c\/b\u003e (\u003ci\u003eJoseph Keshet, Shai Shalev-Shwartz, Yoram Singer and Dan Chazan\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e4.1 Introduction.\u003c\/p\u003e \u003cp\u003e4.2 Problem Setting.\u003c\/p\u003e \u003cp\u003e4.3 Cost and Risk.\u003c\/p\u003e \u003cp\u003e4.4 A Large Margin Approach for Forced Alignment.\u003c\/p\u003e \u003cp\u003e4.5 An Iterative Algorithm.\u003c\/p\u003e \u003cp\u003e4.6 Efficient Evaluation of the Alignment Function.\u003c\/p\u003e \u003cp\u003e4.7 Base Alignment Functions.\u003c\/p\u003e \u003cp\u003e4.8 Experimental Results.\u003c\/p\u003e \u003cp\u003e4.9 Discussion.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 A Kernel Wrapper for Phoneme Sequence Recognition\u003c\/b\u003e (\u003ci\u003eJoseph Keshet and Dan Chazan\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e5.1 Introduction.\u003c\/p\u003e \u003cp\u003e5.2 Problem Setting.\u003c\/p\u003e \u003cp\u003e5.3 Frame-based Phoneme Classifier.\u003c\/p\u003e \u003cp\u003e5.4 Kernel-based Iterative Algorithm for Phoneme Recognition.\u003c\/p\u003e \u003cp\u003e5.5 Nonlinear Feature Functions.\u003c\/p\u003e \u003cp\u003e5.6 Preliminary Experimental Results.\u003c\/p\u003e \u003cp\u003e5.7 Discussion: Canwe Hope for Better Results?\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Augmented Statistical Models: Using Dynamic Kernels for Acoustic Models\u003c\/b\u003e (\u003ci\u003eMark J. F. Gales\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e6.1 Introduction.\u003c\/p\u003e \u003cp\u003e6.2 Temporal Correlation Modeling.\u003c\/p\u003e \u003cp\u003e6.3 Dynamic Kernels.\u003c\/p\u003e \u003cp\u003e6.4 Augmented Statistical Models.\u003c\/p\u003e \u003cp\u003e6.5 Experimental Results.\u003c\/p\u003e \u003cp\u003e6.6 Conclusions.\u003c\/p\u003e \u003cp\u003eAcknowledgements.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Large Margin Training of Continuous Density Hidden Markov Models\u003c\/b\u003e (\u003ci\u003eFei Sha and Lawrence K. Saul\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e7.1 Introduction.\u003c\/p\u003e \u003cp\u003e7.2 Background.\u003c\/p\u003e \u003cp\u003e7.3 Large Margin Training.\u003c\/p\u003e \u003cp\u003e7.4 Experimental Results.\u003c\/p\u003e \u003cp\u003e7.5 Conclusion.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIII Language Modeling.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 A Survey of Discriminative Language Modeling Approaches for Large Vocabulary Continuous Speech Recognition\u003c\/b\u003e (\u003ci\u003eBrian Roark\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e8.1 Introduction.\u003c\/p\u003e \u003cp\u003e8.2 General Framework.\u003c\/p\u003e \u003cp\u003e8.3 Further Developments.\u003c\/p\u003e \u003cp\u003e8.4 Summary and Discussion.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Large Margin Methods for Part-of-Speech Tagging\u003c\/b\u003e (\u003ci\u003eYasemin Altun\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e9.1 Introduction.\u003c\/p\u003e \u003cp\u003e9.2 Modeling Sequence Labeling.\u003c\/p\u003e \u003cp\u003e9.3 Sequence Boosting.\u003c\/p\u003e \u003cp\u003e9.4 Hidden Markov Support Vector Machines.\u003c\/p\u003e \u003cp\u003e9.5 Experiments.\u003c\/p\u003e \u003cp\u003e9.6 Discussion.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 A Proposal for a Kernel Based Algorithm for Large Vocabulary Continuous Speech Recognition\u003c\/b\u003e (\u003ci\u003eJoseph Keshet\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e10.1 Introduction.\u003c\/p\u003e \u003cp\u003e10.2 Segment Models and Hidden Markov Models.\u003c\/p\u003e \u003cp\u003e10.3 Kernel Based Model.\u003c\/p\u003e \u003cp\u003e10.4 Large Margin Training.\u003c\/p\u003e \u003cp\u003e10.5 Implementation Details.\u003c\/p\u003e \u003cp\u003e10.6 Discussion.\u003c\/p\u003e \u003cp\u003eAcknowledgements.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIV Applications.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Discriminative Keyword Spotting\u003c\/b\u003e (\u003ci\u003eDavid Grangier, Joseph Keshet and Samy Bengio\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e11.1 Introduction.\u003c\/p\u003e \u003cp\u003e11.2 Previous Work.\u003c\/p\u003e \u003cp\u003e11.3 Discriminative Keyword Spotting.\u003c\/p\u003e \u003cp\u003e11.4 Experiments and Results.\u003c\/p\u003e \u003cp\u003e11.5 Conclusions.\u003c\/p\u003e \u003cp\u003eAcknowledgements.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Kernel-based Text-independent Speaker Verification\u003c\/b\u003e (\u003ci\u003eJohnny Mariéthoz, Samy Bengio and Yves Grandvalet\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e12.1 Introduction.\u003c\/p\u003e \u003cp\u003e12.2 Generative Approaches.\u003c\/p\u003e \u003cp\u003e12.3 Discriminative Approaches.\u003c\/p\u003e \u003cp\u003e12.4 Benchmarking Methodology.\u003c\/p\u003e \u003cp\u003e12.5 Kernels for Speaker Verification.\u003c\/p\u003e \u003cp\u003e12.6 Parameter Sharing.\u003c\/p\u003e \u003cp\u003e12.7 Is the Margin Useful for This Problem?\u003c\/p\u003e \u003cp\u003e12.8 Comparing all Methods.\u003c\/p\u003e \u003cp\u003e12.9 Conclusion.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Spectral Clustering for Speech Separation\u003c\/b\u003e (\u003ci\u003eFrancis R. Bach and Michael I. Jordan\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e13.1 Introduction.\u003c\/p\u003e \u003cp\u003e13.2 Spectral Clustering and Normalized Cuts.\u003c\/p\u003e \u003cp\u003e13.3 Cost Functions for Learning the Similarity Matrix.\u003c\/p\u003e \u003cp\u003e13.4 Algorithms for Learning the Similarity Matrix.\u003c\/p\u003e \u003cp\u003e13.5 Speech Separation as Spectrogram Segmentation.\u003c\/p\u003e \u003cp\u003e13.6 Spectral Clustering for Speech Separation.\u003c\/p\u003e \u003cp\u003e13.7 Conclusions.\u003c\/p\u003e \u003cp\u003eReferences .\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIndex.\u003c\/b\u003e\u003c\/p\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49525388607831,"sku":"9780470696835","price":100.65,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780470696835.jpg?v=1731860329","url":"https:\/\/bookcurl.com\/products\/automatic-speech-and-speaker-recognition-9780470696835","provider":"Book Curl","version":"1.0","type":"link"}