Sixth European Conference on Speech Communication and Technology

Budapest, Hungary
September 5-9, 1999

Channel Identification and Spectrum Estimation for Robust Automatic Speech Recognition

Yunxin Zhao

Department of Computer Engineering and Computer Science, University of Missouri, Columbia, MO, USA

A feature estimation technique is proposed for speech signals that are corrupted by both additive and convolutive noises via com-bining channel identification with power spectrum estimation. A correlation-matching algorithm is developed for channel identification, and a Gaussian mixture density model of speech DFT spectra is formulated for estimation of speech power spectra. Cepstral features of speech are calculated from the estimated power spec-tra. Using the proposed method, significantly improved accuracy was achieved on speaker-independent continuous speech recognition where the speech data were corrupted by a simulated linear distortion channel and additive white noise.

Full Paper (PDF)   Gnu-Zipped Postscript

Bibliographic reference.  Zhao, Yunxin (1999): "Channel identification and spectrum estimation for robust automatic speech recognition", In EUROSPEECH'99, 2877-2880.