Weighting observation vectors for robust speech recognition in noisy environments

In this paper, we propose a novel approach to robust speech recognition in noisy environments by discriminating the observation vectors. In conventional HMM-based speech recognition, all the observation vectors are treated with equal importance no matter how the corresponding speech segment is corrupted with noise. Our approach proposed here modifies the conventional decoder by weighting the likelihood scores for different observation vectors based on the signal to noise ratios (SNRs) of the corresponding speech frames when the probabilities of generating a sequence of observations are being calculated for some models. The proposed approach combined with spectral subtraction is evaluated with four different kinds of noises added to the clean speech. The experimental results show the superior performance of the proposed method over the method where only the spectral subtraction is applied, especially in the median SNR environments.