Bayesian channel equalisation and robust features for speech recognition

Milner, B. P. and Vaseghi, S. V. (1996) Bayesian channel equalisation and robust features for speech recognition. IEE Proceedings: Vision, Image and Signal Processing, 143 (4). pp. 223-231. ISSN 1350-245X

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Abstract

The use of a speech recognition system with telephone channel environments, or different microphones, requires channel equalisation. In speech recognition, the speech model provides a bank of statistical information that can be used in the channel identification and equalisation process. The authors consider HMM-based channel equalisation, and present results demonstrating that substantial improvement can be obtained through the equalisation process. An alternative method, for speech recognition, is to use a feature set which is more robust to channel distortion. Channel distortions result in an amplitude tilt of the speech cepstrum, and therefore differential cepstral features provide a measure of immunity to channel distortions. In particular the cepstral-time feature matrix, in addition to providing a framework for representing speech dynamics, can be made robust to channel distortions. The authors present results demonstrating that a major advantage of cepstral-time matrices is their channel insensitive character

Item Type:	Article
Faculty \ School:	Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Interactive Graphics and Audio Faculty of Science > Research Groups > Smart Emerging Technologies Faculty of Science > Research Groups > Data Science and AI
Depositing User:	Vishal Gautam
Date Deposited:	08 Mar 2011 08:40
Last Modified:	10 Dec 2024 01:22
URI:	https://ueaeprints.uea.ac.uk/id/eprint/22043
DOI:	10.1049/ip-vis:19960577

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