Robust Automatic Speech Recognition: A Bridge to Practical Applications establishes a solid foundation for automatic speech recognition that is robust against acoustic environmental distortion. It provides a thorough overview of classical and modern noise-and reverberation robust techniques that have been developed over the past thirty years, with an emphasis on practical methods that have been proven to be successful and which are likely to be further developed for future applications.The strengths and weaknesses of robustness-enhancing speech recognition techniques are carefully analyzed. The book covers noise-robust techniques designed for acoustic models which are based on both Gaussian mixture models and deep neural networks. In addition, a guide to selecting the best methods for practical applications is provided.The reader will:

Advances in training models with log-linear structures, with topics including variable selection, the geometry of neural nets, and applications.

Log-linear models play a key role in modern big data and machine learning applications. From simple binary classification models through partition functions, conditional random fields, and neural nets, log-linear structure is closely related to performance in certain applications and influences fitting techniques used to train models. This volume covers recent advances in training models with log-linear structures, covering the underlying geometry, optimization techniques, and multiple applications. The first chapter shows readers the inner workings of machine learning, providing insights into the geometry of log-linear and neural net models. The other chapters range from introductory material to optimization techniques to involved use cases. The book, which grew out of a NIPS workshop, is suitable for graduate students doing research in machine learning, in particular deep learning, variable selection, and applications to speech recognition. The contributors come from academia and industry, allowing readers to view the field from both perspectives.

ContributorsAleksandr Aravkin, Avishy Carmi, Guillermo A. Cecchi, Anna Choromanska, Li Deng, Xinwei Deng, Jean Honorio, Tony Jebara, Huijing Jiang, Dimitri Kanevsky, Brian Kingsbury, Fabrice Lambert, Aurélie C. Lozano, Daniel Moskovich, Yuriy S. Polyakov, Bhuvana Ramabhadran, Irina Rish, Dimitris Samaras, Tara N. Sainath, Hagen Soltau, Serge F. Timashev, Ewout van den Berg

This book analyses the global diffusion of key competencies-based education (CBE) as a “global education policy” (GEP), focusing on China’s process of adoption and adaptation.

Based on a six-year empirical study combining interviews, observations, and document analysis, it examines how national and local actors interpret, modify, and resist CBE. Constructing and applying a four-stage framework for the GEP transfer process, the book integrates macro-level analysis of global forces with micro-level analysis of specific policy changes at the national level, and bridges theoretical and practical perspectives through both macro-level policy analysis and micro-level case studies.

The book provides valuable insights and implications of policy formulation and educational practice for educators, policy makers, and researchers interested in the dynamics of global policy transfer, localised educational reforms, and the complexities of reform in a globalised world. The case study of China’s implementation and localisation of CBE will also inform global efforts to adapt and integrate CBE in diverse educational contexts.

This book analyses the global diffusion of key competencies-based education (CBE) as a “global education policy” (GEP), focusing on China’s process of adoption and adaptation.

Based on a six-year empirical study combining interviews, observations, and document analysis, it examines how national and local actors interpret, modify, and resist CBE. Constructing and applying a four-stage framework for the GEP transfer process, the book integrates macro-level analysis of global forces with micro-level analysis of specific policy changes at the national level, and bridges theoretical and practical perspectives through both macro-level policy analysis and micro-level case studies.

The book provides valuable insights and implications of policy formulation and educational practice for educators, policy makers, and researchers interested in the dynamics of global policy transfer, localised educational reforms, and the complexities of reform in a globalised world. The case study of China’s implementation and localisation of CBE will also inform global efforts to adapt and integrate CBE in diverse educational contexts.

This book provides a comprehensive overview of the recent advancement in the field of automatic speech recognition with a focus on deep learning models including deep neural networks and many of their variants. This is the first automatic speech recognition book dedicated to the deep learning approach. In addition to the rigorous mathematical treatment of the subject, the book also presents insights and theoretical foundation of a series of highly successful deep learning models.

In recent years, deep learning has fundamentally changed the landscapes of a number of areas in artificial intelligence, including speech, vision, natural language, robotics, and game playing. In particular, the striking success of deep learning in a wide variety of natural language processing (NLP) applications has served as a benchmark for the advances in one of the most important tasks in artificial intelligence. 

The book appeals to advanced undergraduate and graduate students, post-doctoral researchers, lecturers and industrial researchers, as well as anyone interested in deep learning and natural language processing. 

Robust Automatic Speech Recognition: A Bridge to Practical Applications establishes a solid foundation for automatic speech recognition that is robust against acoustic environmental distortion. It provides a thorough overview of classical and modern noise-and reverberation robust techniques that have been developed over the past thirty years, with an emphasis on practical methods that have been proven to be successful and which are likely to be further developed for future applications.The strengths and weaknesses of robustness-enhancing speech recognition techniques are carefully analyzed. The book covers noise-robust techniques designed for acoustic models which are based on both Gaussian mixture models and deep neural networks. In addition, a guide to selecting the best methods for practical applications is provided.The reader will:

Advances in training models with log-linear structures, with topics including variable selection, the geometry of neural nets, and applications.

Log-linear models play a key role in modern big data and machine learning applications. From simple binary classification models through partition functions, conditional random fields, and neural nets, log-linear structure is closely related to performance in certain applications and influences fitting techniques used to train models. This volume covers recent advances in training models with log-linear structures, covering the underlying geometry, optimization techniques, and multiple applications. The first chapter shows readers the inner workings of machine learning, providing insights into the geometry of log-linear and neural net models. The other chapters range from introductory material to optimization techniques to involved use cases. The book, which grew out of a NIPS workshop, is suitable for graduate students doing research in machine learning, in particular deep learning, variable selection, and applications to speech recognition. The contributors come from academia and industry, allowing readers to view the field from both perspectives.

ContributorsAleksandr Aravkin, Avishy Carmi, Guillermo A. Cecchi, Anna Choromanska, Li Deng, Xinwei Deng, Jean Honorio, Tony Jebara, Huijing Jiang, Dimitri Kanevsky, Brian Kingsbury, Fabrice Lambert, Aurélie C. Lozano, Daniel Moskovich, Yuriy S. Polyakov, Bhuvana Ramabhadran, Irina Rish, Dimitris Samaras, Tara N. Sainath, Hagen Soltau, Serge F. Timashev, Ewout van den Berg

This book analyses the global diffusion of key competencies-based education (CBE) as a “global education policy” (GEP), focusing on China’s process of adoption and adaptation.

Based on a six-year empirical study combining interviews, observations, and document analysis, it examines how national and local actors interpret, modify, and resist CBE. Constructing and applying a four-stage framework for the GEP transfer process, the book integrates macro-level analysis of global forces with micro-level analysis of specific policy changes at the national level, and bridges theoretical and practical perspectives through both macro-level policy analysis and micro-level case studies.

The book provides valuable insights and implications of policy formulation and educational practice for educators, policy makers, and researchers interested in the dynamics of global policy transfer, localised educational reforms, and the complexities of reform in a globalised world. The case study of China’s implementation and localisation of CBE will also inform global efforts to adapt and integrate CBE in diverse educational contexts.

This book analyses the global diffusion of key competencies-based education (CBE) as a “global education policy” (GEP), focusing on China’s process of adoption and adaptation.

Based on a six-year empirical study combining interviews, observations, and document analysis, it examines how national and local actors interpret, modify, and resist CBE. Constructing and applying a four-stage framework for the GEP transfer process, the book integrates macro-level analysis of global forces with micro-level analysis of specific policy changes at the national level, and bridges theoretical and practical perspectives through both macro-level policy analysis and micro-level case studies.

The book provides valuable insights and implications of policy formulation and educational practice for educators, policy makers, and researchers interested in the dynamics of global policy transfer, localised educational reforms, and the complexities of reform in a globalised world. The case study of China’s implementation and localisation of CBE will also inform global efforts to adapt and integrate CBE in diverse educational contexts.

This book provides a comprehensive overview of the recent advancement in the field of automatic speech recognition with a focus on deep learning models including deep neural networks and many of their variants. This is the first automatic speech recognition book dedicated to the deep learning approach. In addition to the rigorous mathematical treatment of the subject, the book also presents insights and theoretical foundation of a series of highly successful deep learning models.

In recent years, deep learning has fundamentally changed the landscapes of a number of areas in artificial intelligence, including speech, vision, natural language, robotics, and game playing. In particular, the striking success of deep learning in a wide variety of natural language processing (NLP) applications has served as a benchmark for the advances in one of the most important tasks in artificial intelligence. 

The book appeals to advanced undergraduate and graduate students, post-doctoral researchers, lecturers and industrial researchers, as well as anyone interested in deep learning and natural language processing.