Monte Carlo model-space noise adaptation for speech recognition
Daniel Povey, Brian E. D. Kingsbury · 2008
We describe a Monte Carlo method for model-space noise adaptation of Gaussian mixture models (GMMs). This method combines a single-Gaussian noise model with the GMM speech model to produce an adapted model. It is similar to Parallel Model Combination or model-space Joint, except that it applies to spliced and projected MFCC features rather than to MFCC plus dynamic features. We demonstrate the necessity of reestimating the noise using both the silence and speech frames rather than just estimating it from silence frames, and obtain improvements on a matched test set without added noise using a system that includes all standard adaptation techniques.