Dynamic speaker adaptation for isolated letter recognition using MAP estimation

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.