We describe a Monte Carlo method formodel-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 re-estimating 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.
Bibliographic reference. Povey, Daniel / Kingsbury, Brian (2008): "Monte Carlo model-space noise adaptation for speech recognition", In INTERSPEECH-2008, 1281-1284.