New Era for Robust Speech Recognition: Exploiting Deep Learning
Editat de Shinji Watanabe, Marc Delcroix, Florian Metze, John R. Hersheyen Limba Engleză Hardback – 9 noi 2017
This book is intended for researchers and practitioners working in the field of speech processing and recognition who are interested in the latest deep learning techniques for noise robustness. It will also be of interest to graduate students in electrical engineering or computer science, who will find it a useful guide to this field of research.
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Specificații
ISBN-13: 9783319646794
ISBN-10: 3319646796
Pagini: 436
Ilustrații: XVII, 436 p. 76 illus., 26 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.81 kg
Ediția:1st ed. 2017
Editura: Springer International Publishing
Colecția Springer
Locul publicării:Cham, Switzerland
ISBN-10: 3319646796
Pagini: 436
Ilustrații: XVII, 436 p. 76 illus., 26 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.81 kg
Ediția:1st ed. 2017
Editura: Springer International Publishing
Colecția Springer
Locul publicării:Cham, Switzerland
Cuprins
Speech and Language Processing.- Automatic Speech Recognition (ASR).- Recent Applications.- Signal-Processing-Based Front-End for Robust ASR.- Generative Model-Based Speech Enhancement.- Denoising Autoencoder.- Discriminative Microphone Array Enhancement.- Learning Robust Feature Representation.- Training Data Augmentation.- Adaptation and Augmented Features.- Novel Model Topologies.- Novel Objective Criteria.- Benchmark Data, Tools, and Systems.
Textul de pe ultima copertă
This book covers the state-of-the-art in deep neural-network-based methods for noise robustness in distant speech recognition applications. It provides insights and detailed descriptions of some of the new concepts and key technologies in the field, including novel architectures for speech enhancement, microphone arrays, robust features, acoustic model adaptation, training data augmentation, and training criteria. The contributed chapters also include descriptions of real-world applications, benchmark tools and datasets widely used in the field.
This book is intended for researchers and practitioners working in the field of speech processing and recognition who are interested in the latest deep learning techniques for noise robustness. It will also be of interest to graduate students in electrical engineering or computer science, who will find it a useful guide to this field of research.
This book is intended for researchers and practitioners working in the field of speech processing and recognition who are interested in the latest deep learning techniques for noise robustness. It will also be of interest to graduate students in electrical engineering or computer science, who will find it a useful guide to this field of research.
Caracteristici
Field of automatic speech recognition has evolved greatly since the introduction of deep learning
Covers the state-of-the-art in noise robustness for deep neural-network-based speech recognition
Includes descriptions of benchmark tools and datasets widely used in the field
Covers the state-of-the-art in noise robustness for deep neural-network-based speech recognition
Includes descriptions of benchmark tools and datasets widely used in the field