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A feature estimation technique is proposed for speech signals that are corrupted by both additive and convolutive noises via combining 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 spectra. 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.