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 Recognitionpresents a contemporary and comprehensive description of both theoretic abstraction and practical issues inherent in the distant ASR problem.
Key Features:
- Covers the entire topic of distant ASR and offers practical solutions to overcome the problems related to it
- Provides documentation and sample scripts to enable readers to construct state-of-the-art distant speech recognition systems
- Gives relevant background information in acoustics and filter techniques,
- Explains the extraction and enhancement of classification relevant speech features
- Describes maximum likelihood as well as discriminative parameter estimation, and maximum likelihood normalization techniques
- Discusses the use of multi-microphone configurations for speaker tracking and channel combination
- Presents several applications of the methods and technologies described in this book
- 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.
Matthias Wölfel will finish his Ph.D on far-field speech recognition in the middle of 2007. He has given seminars in Speech and Robust Speech Recognition and has published more than 25 papers in this field. He has been involved in two European research projects on distant speech recognition: FAME and CHIL. He has written extensive sections of source code of speech enhancement, spectral estimation and feature extraction for automatic recognition, and has participated in NIST Rich Transcription evaluations with focus on far-field speech.
John McDonough holds a Ph.D. in electrical and computer engineering from the Johns Hopkins Univerity. He has taught the courses Man-Machine Communication and Microphone Arrays: Gateway to Hands Free Automatic Speech Recognition at the University of Karlsruhe for five years. He has published dozens of conference and journal articles, and written complete software toolkits for source localization, beamforming and automatic speech recognition.
