Speech signals transmitted over telephone network often suffer from interference due to ambient noise and channel distortion. In this paper, a novel frame-dependent fuzzy channel compensation (FD-FCC) method employing two-stage bias subtraction is proposed to minimize the channel effect. First, through maximum likelihood (ML) estimation over the set of all word models, we choose the word model which is best matched with the input utterance. Then, based upon this word model, a set of mixture biases can be derived by averaging the cepstral differences between the input utterance and the chosen model. In the second stage, instead of using a single bias, a frame-dependent bias is calculated for each input frame to equalize the channel variations in the input utterance. This frame-dependent bias is achieved by the convex combination of those mixture biases which are weighted by a fuzzy membership function. Experimental results show that the channel effect can be effectively canceled even though the additive background noise is involved in a telephone speech recognition system.

An efficient and simple approach to consonant/vowel (C/V) segmentation by incorporating the SNR improvement of a speech enhancement system with the energy variation of two adjacent frames is proposed. Experimental results show that the proposed scheme performs well in segmenting C/V for a spontaneously spoken utterance.

Employing noise masking threshold (NMT) to adapt a speech enhancement system has become popular due to the advantage of rendering the residual noise to perceptually white. Most methods employ the NMT to empirically adjust the parameters of a speech enhancement system according to the various properties of noise. In this article, without any predefined empirical factor, an explicit-form gain factor for a frequency bin is derived by perceptually constraining the residual noise below the NMT in spectral domain. This perceptual constraint preserves the spectrum of noisy speech when the level of residual noise is less than the NMT. If the level of residual noise exceeds the NMT, then the spectrum of noisy speech is suppressed to reduce the corrupting noise. Experimental results show that the proposed approach can efficiently remove the added noise in cases of various noise corruptions, and almost free from musical residual noise.