Frequency-domain maximum likelihood estimation for automatic speech recognition in additive and convolutive noises

A feature estimation technique is proposed for speech signals that are degraded by both additive and convolutive noises. An EM algorithm is formulated in the frequency-domain for identification of the magnitude response of the distortion channel and power spectrum of additive noise, and posterior estimates of short-time power spectra of speech are obtained based on the identified channel and noise. The estimated posterior power spectra are used to calculate perceptually-based linear prediction cepstral coefficients, and the estimated cepstral features and their temporal regression coefficients are used for automatic speech recognition using acoustic models trained from clean speech. Experiments were performed on speaker independent continuous speech recognition, where the speech data were taken from the TIMIT database and were degraded by a distortion channel and simulated additive noises with white or colored spectral characteristics at various SNR levels. Experimental results indicate that the proposed technique leads to convergent identification of channel and noise and significantly improved recognition accuracy for speaker-independent continuous speech