Unsupervised adaptation to new speakers in feature-based letter recognition

This paper describes two new methods by which the CMU feature-based recognition system can learn the acoustical characteristics of individual speakers without feedback from the user. We have previously described how the system uses MAP techniques to update its estimates of the mean values of features used by the classifier in recognizing the letters of the English alphabet on the basis of a priori information and labelled observations. In the first of the new procedures described in this paper the system assumes a correct decision every time it classifies a new utterance with a sufficiently high confidence level. In the second new procedure the system adjusts its estimates of the means on the basis of their correlation with the average values of the features over all utterances. Experiments were conducted on two confusable sets of letters using both speaker adaptation procedures. In each case classification performance using the unsupervised estimation procedures could equal that obtained using speaker adaptation with feedback from the user, although which method provided the better performance depended on which set of letters was being classified.