This 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 fieldCovers important aspects of extending the binary support vector machine to speech and speaker recognition applicationsDiscusses large margin and kernel method algorithms for sequence prediction required for acoustic modelingReviews past and present work on discriminative training of language models, and describes different large margin algorithms for the application of part-of-speech taggingSurveys recent work on the use of kernel approaches to text-independent speaker verification, and introduces the main concepts and algorithmsSurveys recent work on kernel approaches to learning a similarity matrix from dataThis book will be of interest to researchers, practitioners, engineers, and scientists in speech processing and machine learning fields.

This 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.

This book contains a selection of refereed papers presented at the 1st Wo- shop on Machine Learning for Multimodal Interaction (MLMI 2004), held at the "Centre du Parc," Martigny, Switzerland, during June 21-23, 2004. The workshop was organized and sponsored jointly by three European projects, - AMI, Augmented Multiparty Interaction, http://www.amiproject.org - PASCAL, Pattern Analysis, Statistical Modeling and Computational Learning, http://www.pascal-network.org - M4, Multi-modal Meeting Manager, http://www.m4project.org as well as the Swiss National Centre of Competence in Research (NCCR): - IM2: Interactive Multimodal Information Management, http://www.im2.ch MLMI 2004 was thus sponsored by the European Commission and the Swiss National Science Foundation. Given the multiple links between the above projects and several related - search areas, it was decided to organize a joint workshop bringing together - searchers from the di?erent communities working around the common theme of advanced machine learning algorithms for processing and structuring mul- modal human interaction in meetings.The motivation for creating such a forum, which could be perceived as a number of papers from di?erent research dis- plines, evolved from a real need that arose from these projects and the strong motivation of their partners for such a multidisciplinary workshop. This asse- ment was indeed con?rmed by the success of this ?rst MLMI workshop, which attracted more than 200 participants.

This book constitutes the thoroughly refereed post-proceedings of the Second International Workshop on Machine Learning for Multimodal Interaction held in July 2005. The 38 revised full papers presented together with two invited papers were carefully selected during two rounds of reviewing and revision. The papers are organized in topical sections on multimodal processing, HCI and applications, discourse and dialogue, emotion, visual processing, speech and audio processing, and NIST meeting recognition evaluation.

This book constitutes the thoroughly refereed post-proceedings of the Third International Workshop on Machine Learning for Multimodal Interaction, MLMI 2006, held in Bethesda, MD, USA, in May 2006. The papers are organized in topical sections on multimodal processing, image and video processing, HCI and applications, discourse and dialogue, speech and audio processing, and NIST meeting recognition evaluation.

This 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 fieldCovers important aspects of extending the binary support vector machine to speech and speaker recognition applicationsDiscusses large margin and kernel method algorithms for sequence prediction required for acoustic modelingReviews past and present work on discriminative training of language models, and describes different large margin algorithms for the application of part-of-speech taggingSurveys recent work on the use of kernel approaches to text-independent speaker verification, and introduces the main concepts and algorithmsSurveys recent work on kernel approaches to learning a similarity matrix from dataThis book will be of interest to researchers, practitioners, engineers, and scientists in speech processing and machine learning fields.

This 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.

This book contains a selection of refereed papers presented at the 1st Wo- shop on Machine Learning for Multimodal Interaction (MLMI 2004), held at the "Centre du Parc," Martigny, Switzerland, during June 21-23, 2004. The workshop was organized and sponsored jointly by three European projects, - AMI, Augmented Multiparty Interaction, http://www.amiproject.org - PASCAL, Pattern Analysis, Statistical Modeling and Computational Learning, http://www.pascal-network.org - M4, Multi-modal Meeting Manager, http://www.m4project.org as well as the Swiss National Centre of Competence in Research (NCCR): - IM2: Interactive Multimodal Information Management, http://www.im2.ch MLMI 2004 was thus sponsored by the European Commission and the Swiss National Science Foundation. Given the multiple links between the above projects and several related - search areas, it was decided to organize a joint workshop bringing together - searchers from the di?erent communities working around the common theme of advanced machine learning algorithms for processing and structuring mul- modal human interaction in meetings.The motivation for creating such a forum, which could be perceived as a number of papers from di?erent research dis- plines, evolved from a real need that arose from these projects and the strong motivation of their partners for such a multidisciplinary workshop. This asse- ment was indeed con?rmed by the success of this ?rst MLMI workshop, which attracted more than 200 participants.

This book constitutes the thoroughly refereed post-proceedings of the Second International Workshop on Machine Learning for Multimodal Interaction held in July 2005. The 38 revised full papers presented together with two invited papers were carefully selected during two rounds of reviewing and revision. The papers are organized in topical sections on multimodal processing, HCI and applications, discourse and dialogue, emotion, visual processing, speech and audio processing, and NIST meeting recognition evaluation.

This book constitutes the thoroughly refereed post-proceedings of the Third International Workshop on Machine Learning for Multimodal Interaction, MLMI 2006, held in Bethesda, MD, USA, in May 2006. The papers are organized in topical sections on multimodal processing, image and video processing, HCI and applications, discourse and dialogue, speech and audio processing, and NIST meeting recognition evaluation.