In this letter, a novel perceptually motivated single channel speech enhancement approach based on Deep Neural Network (DNN) is presented. Taking into account the good masking properties of the human auditory system, a new DNN architecture is proposed to reduce the perceptual effect of the residual noise. This new DNN architecture is directly trained to learn a gain function which is used to estimate the power spectrum of clean speech and shape the spectrum of the residual noise at the same time. Experimental results demonstrate that the proposed perceptually motivated speech enhancement approach could achieve better objective speech quality when tested with TIMIT sentences corrupted by various types of noise, no matter whether the noise conditions are included in the training set or not.

In this letter, we propose a novel approach to speech enhancement, which incorporates a new criterion based on residual noise shaping. In the proposed approach, our goal is to make the residual noise perceptually comfortable instead of making it less audible. A predetermined `comfort noise' is provided as a target for the spectral shaping. Based on some assumptions, the resulting spectral gain function turns out to be a slight modification of the Wiener filter while requiring very low computational complexity. Subjective listening test shows that the proposed algorithm outperforms the conventional spectral enhancement technique based on soft decision and the noise suppression implemented in IS-893 Selectable Mode Vocoder.

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.