Estimation of hidden Markov model parameters by minimizing empirical error rate

An approach for designing a set of acoustic models for speech recognition applications which results in a minimal empirical error rate for a given decoder and training data is studied. In an evaluation of the system for an isolated word recognition task, hidden Markov models (HMMs) are used to characterize the probability density functions of the acoustic signals from the different words in the vocabulary. Decoding is performed by applying the maximum aposteriori decision rule to the acoustic models. The HMMs are estimated by minimizing a differentiable cost function, which approximates the empirical error rate function, using the steepest descent method. The HMMs designed by the minimum empirical error rate approach were used in multispeaker recognition of the English E-set words and compared to models designed by the standard maximum-likelihood estimation approach. The approach increased recognition accuracy from 68.2% to 76.2% on the training set and from 53.4% to 56.4% on an independent set of test data