New Era for Robust Speech Recognition

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.

• Speech Recognition
• Speech Processing
• Natural Language Processing (NLP)
• Automatic Speech Recognition (ASR)
• Signal Processing
• Deep Learning
• Noise Robustness
• Neural Networks (NNs)
• Distant Speech
• Acoustic Model Adaptation

• Mitsubishi Electric Research Laboratories (MERL), Cambridge, USA

  Shinji Watanabe, John R. Hershey

• NTT Communication Science Laboratories, NTT Corporation, Kyoto, Japan

  Marc Delcroix

• Language Technologies Institute, Carnegie Mellon University, Pittsburgh, USA

  Florian Metze

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