Dynamic adaptation of Hidden Markov models for robust isolated-word speech recognition

The authors describe an HMM-based isolated-word recognition system that dynamically adapts word model parameters to new speakers and to stress-induced speech variations. During recognition all input tokens presented to the system can be used to augment the current word model parameters. New tokens can be weighted so that adaptation simply increases the size of the training set, or tracks systematic changes by exponentially weighting all previously seen data. This system was tested on the 35-word 10710 token Lincoln stressed speech data base. Speaker adaptation experiments produced error rates equivalent to speaker-trained systems after the presentation of only a single new token per vocabulary word. Stress condition adaptation experiments produced results comparable to multistyle-trained systems after the presentation of several new tokens per vocabulary word.<>