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