Likelihood decision boundary estimation between HMM pairs in speech recognition

In maximum likelihood (ML) estimation of hidden Markov models (HMMs) for speech recognition, the criterion is to maximize the total probability across the training data for a particular speech unit, such as a word, monophone, diphone, or triphone. Since each unit model is trained separately, such a strategy can often lead to biases among decision boundaries of the generated model set. In this correspondence, we propose a new technique to minimize the total number of misclassifications in the training data set by adjusting the decision boundaries between HMM pairs. The proposed algorithm is shown to reduce the error rate in a number of speech recognition tasks such as accent detection, language identification, and confusable word pair discrimination. The technique is also attractive because it is simple to implement and the improvement in performance is achieved without any added complexity in the decoding phase.