4th International Conference on Spoken Language Processing

Philadelphia, PA, USA
October 3-6, 1996

A Comparitive Analysis of Channel-Robust Features and Channel Equalization Methods for Speech Recognition

Saeed V. Vaseghi (1), Ben Milner (1,2)

(1) School of Electrical Engineering and Computer Science, Queen's University of Belfast, UK
(2) British Telecom Research Laboratories, UK

The use of a speech recognition system with telephone channel environments, or different microphones, requires channel equalisation. In speech recognition, the speech models provide a bank of statistical information that can be used in the channel identification and equalisation process. in this paper we consider HMM-based channel equalistaion, and present results demonstrating that substantial improvement can be obtained through the equalisation process. An alternative method is to use a set of features which is more robust to channel distortion. Channel distortions result in an amplitude-tilt of the speech cepstrum, and so differential cepstral features should 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. We present results demonstrating that a major advantage of cepstral-time matrices is their channel insensitive character.

Full Paper

Bibliographic reference.  Vaseghi, Saeed V. / Milner, Ben (1996): "A comparitive analysis of channel-robust features and channel equalization methods for speech recognition", In ICSLP-1996, 877-880.