VAD-measure-embedded decoder with online model adaptation

We previously proposed a decoding method for automatic speech recognition utilizing hypothesis scores weighted by voice activity detection (VAD)-measures. This method uses two Gaussian mixture models (GMMs) to obtain confidence measures: one for speech, the other for non-speech. To achieve good search performance, we need to adapt the GMMs properly for input utterances and environmental noise. We describe a new unsupervised on-line GMM adaptation method based on MAP estimation. The robustness of our method is further improved by weighting updating parameters of GMMs according to the confidence measure for the adaptation data. We also describe an approach to accelerate the adaptation by caching statistical values to adapt GMMs. Experimental results on Drivers’ Japanese Speech Corpus in a Car Environment (DJSC) show that the adaptation with decoding method significantly improves the word accuracy from 54.8% to 59.6%. Moreover, the weighting method improves the robustness of the unsupervised adaptation, and the cache method greatly accelerates the decoding process. Consequently, our adaptive decoding method significantly improves the word accuracy in a noisy environment with only a minor increase in the computational cost.