Model based approach for robust speech recognition in noisy environments

Abstract

Presently, the problem of noise robustness is one of the most important issues in speech recognition. In this dissertation work, we devote to solve noise robustness problems in speech recognition based on speech feature vector transform(data transform) and model parameter compensation (distribution transform). First, we presented novel data transformation algorithm which estimates clean speech feature vector from corrupted one. Nonlinear contamination procedure of speech signal in noisy environment was approximated to linear function based on Taylor series expansion. Additive noise was modeled as a Gaussian distribution and spectral tilt was assumed fixed unknown vector. In this case, additive noise mean and variance, and spectral tilt are called by environmental variables those are estimated iteratively in maximum likelihood sense. Different from previous method, we incorporated variance of additive noise into re-estimation procedure with which we had more rigorous solution for environmental variables. We called this algorithm model-based linear approximation(MLA) method. Although the MLA methods was originally devised to compensate speech feature vector without a priori knowledge about noisy environment, we could easily combine the MLA methods with a priori knowledge by Bayesian estimation method. Also, the MLA method was extended to multiple noise condition. Each noise source was assumed to have an independent Gaussian distribution, and mean and variance of each noise source were considered as environmental variables. Experimental results showed that performance of MLA is comparable to that of stereo-data-based data transform algorithm. It is worthy of note that stereo-data-based data transform algorithm resulted in poor performance when there is insufficient adaptation data, while MLA does not need any adaptation data. Comparison with other on-line algorithm was also conducted and it was observed that MLA outperformed other methods especially at low SNR co...