Titlebook: New Era for Robust Speech Recognition; Exploiting Deep Lear Shinji Watanabe,Marc Delcroix,John R. Hershey Book 2017 Springer International

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Raw Multichannel Processing Using Deep Neural Networksfrom acoustic modeling. In this chapter, we perform multichannel enhancement jointly with acoustic modeling in a deep-neural-network framework. Inspired by beamforming, which leverages differences in the fine time structure of the signal at different microphones to filter energy arriving from differ

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Adaptation of Deep Neural Network Acoustic Models for Robust Automatic Speech Recognitionrecognition (ASR). However, DNN adaptation remains a challenging task. Many approaches have been proposed in recent years to improve the adaptability of DNNs to achieve robust ASR. This chapter will review the recent adaptation methods for DNNs, broadly categorising them into constrained adaptation,

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Training Data Augmentation and Data Selectiontions. Our work, conducted during the JSALT 2015 workshop, aimed at the development of: (1) Data augmentation strategies including noising and reverberation. They were tested in combination with two approaches to signal enhancement: a carefully engineered WPE dereverberation and a learned DNN-based

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Advanced Recurrent Neural Networks for Automatic Speech Recognitionnternal state of the network which allows it to exhibit dynamic temporal behavior. In this chapter, we describe several advanced RNN models for distant speech recognition (DSR). The first set of models are extensions of the prediction-adaptation-correction RNNs (PAC-RNNs). These models were inspired

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End-to-End Architectures for Speech Recognitionoefficient features), natural language processing (.-gram language models), or statistics (hidden markov models). Because of this “compartmentalization,” it is widely accepted that components of an ASR system will largely be optimized individually and in isolation, which will negatively influence ov

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