This work gives a complete overview of distant automatic speech recognition. The performance of conventional Automatic Speech Recognition (ASR) systems degrades dramatically as soon as the microphone is moved away from the mouth of the speaker. This is due to a broad variety of effects such as background noise, overlapping speech from other speakers, and reverberation. While traditional ASR systems underperform for speech captured with far-field sensors, there are a number of novel techniques within the recognition system as well as techniques developed in other areas of signal processing that can mitigate the deleterious effects of noise and reverberation, as well as separating speech from overlapping speakers. Distant Speech Recognition presents a contemporary and comprehensive description of both theoretic abstraction and practical issues inherent in the distant ASR problem. The key features of this work are: coverage of the entire topic of distant ASR and offers practical solutions to overcome the problems related to it; documentation and sample scripts to enable readers to construct state-of-the-art distant speech recognition systems; relevant background information in acoustics and filter techniques; explanation of the extraction and enhancement of classification relevant speech features; description of the maximum likelihood as well as discriminative parameter estimation, and maximum likelihood normalization techniques; discussions on the use of multi-microphone configurations for speaker tracking and channel combination; and, details of several applications of the methods and technologies described in this book. There is also an accompanying website with open source software and tools to construct state-of-the-art distant speech recognition systems. This reference will be an invaluable resource for researchers, developers, engineers and other professionals, as well as advanced students in speech technology, signal processing, acoustics, statistics and artificial intelligence fields.
