In this paper, we propose a new feature extraction method that combines both HMT-based denoising and weighted filter bank analysis for robust speech recognition. The proposed method is made up of two stages in cascade. The first stage is denoising process based on the wavelet domain Hidden Markov Tree model, and the second one is the filter bank analysis with weighting coefficients obtained from the residual noise in the first stage. To evaluate performance of the proposed method, recognition experiments were carried out for additive white Gaussian and pink noise with signal-to-noise ratio from 25 dB to 0 dB. Experiment results demonstrate the superiority of the proposed method to the conventional ones.

This paper describes a new speech enhancement system that employs a microphone array with post-processing based on minimum mean-square error short-time spectral amplitude (MMSE-STSA) estimator. To get more accurate MMSE-STSA estimator in a microphone array, modification and refinement procedure are carried out from each microphone output. Performance of the proposed system is compared with that of other methods using a microphone array. Noise removal experiments for white and pink noises demonstrate the superiority of the proposed speech enhancement system to others with a microphone array in average output SNRs and cepstral distance measures.