Description
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.
Table of Contents
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.
