A probabilistic acoustic map based discriminative HMM training

A hidden Markov model (HMM) training procedure is proposed for improving the discriminative power of a maximum-likelihood (ML)-based HMM. The discriminative HMM consists of two component models, a master model and a slave model. The master model is the conventional ML model. The slave model is obtained by aligning training tokens of a word with all but the correct word master models. All models are trained by projecting acoustic observations onto a set of common probabilistic basis functions, which is called a probabilistic acoustic map, and the output probability of a model is represented as a weighted sum of the basis functions. The proposed algorithm was tested on a 39-word, alpha-digit database of 100 speakers (50 male and 50 female). Specifically, the highly confusable E-set words were separately tested. Experimental results indicate that the new training procedure improved the recognition accuracy of the E-set words by 4.3% and of all 39 words by 3.6%