A confidence-score based unsupervised MAP adaptation for speech recognition
In this paper, a method of confidence-score based MAP (maximum a posteriori) adaptation in speech recognition is proposed and evaluated. Using confidence scores to dynamically decide the weight of the priors is shown to have good performance improvement in an unsupervised incremental adaptation. The side effect of vocabulary mismatch in adaptation is also effectively controlled by this way. This paper first gives theoretical analysis and then shows some experimental results. Several extensions are made and also discussed.