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.

This paper describes the use of maximum a posteriori probability (MAP) techniques to estimate the mean values of features used in statistical pattern classification problems, when these mean feature values from the various decision classes are jointly Gaussian random vectors that are correlated across the decision classes. A set of mathematical formalisms is proposed and used to derive closed-form expressions for the estimates of the class-conditional mean vectors, and for the covariance matrix of the errors of these estimates. Finally, the performance of these algorithms is described for the simple case of a two-class one-feature pattern recognition problem, and compared to the performance of classical estimators that do not exploit the class-to-class correlations of the features' mean values.

A dynamic speaker-adaptation algorithm for the C-MU feature-based isolated letter recognition system, FEATURE, is described. The algorithm, based on maximum a posteriori probability estimation techniques, uses the labelled observations input thus far to the classifier, as well as the a priori correlations of the features within and across the various letters or sets of letters (classes). The probability density functions (pdf) of all the classes are updated simultaneously rather than on a class-by-class basis so that the pdf of a given class is updated before any observation from that class has been input. A significant improvement in the recognition performance was observed for different vocabularies as the system tuned to the the characteristics of a new speaker. Finally, the algorithm was compared to simpler forms of dynamic adaptation. It produced a faster decrease of the error rate than the other tuning procedures. After a small number of iterations, however, the various procedures yielded similar results.