Enhancing the discrimination of speaker independent hidden Markov models with corrective training

Corrective training is a recently proposed method of improving hidden Markov model parameters. Corrective training and related algorithms are applied to the domain of small-vocabulary, speaker-independent recognition. The contribution of each parameter of the algorithm is examined. Results confirm that corrective training can improve on the recognition rate achieved by maximum-likelihood training. However, the algorithm is sensitive to selection of parameters. A heuristic quantity is proposed to monitor the progress of the corrective training algorithm, and this quantity is used to adapt a parameter of corrective training. An alternative training algorithm is discussed and compared to corrective training. It yielded open test recognition rates comparable to those of maximum-likelihood training, but inferior to those of corrective training