Approximate Test Risk Bound Minimization Through Soft Margin Estimation

Inspired by the great success of margin-based classifiers, there is a trend to incorporate the margin concept into hidden Markov modeling for speech recognition. Several attempts based on margin maximization were proposed recently. In this paper, a new discriminative learning framework, called soft margin estimation (SME), is proposed for estimating the parameters of continuous-density hidden Markov models. The proposed method makes direct use of the successful ideas of soft margin in support vector machines to improve generalization capability and decision feedback learning in minimum classification error training to enhance model separation in classifier design. SME is illustrated from a perspective of statistical learning theory. By including a margin in formulating the SME objective function, SME is capable of directly minimizing an approximate test risk bound. Frame selection, utterance selection, and discriminative separation are unified into a single objective function that can be optimized using the generalized probabilistic descent algorithm. Tested on the TIDIGITS connected digit recognition task, the proposed SME approach achieves a string accuracy of 99.43%. On the 5 k-word Wall Street Journal task, SME obtains relative word error rate reductions of about 10% over our best baseline results in different experimental configurations. We believe this is the first attempt to show the effectiveness of margin-based acoustic modeling for large vocabulary continuous speech recognition in a hidden Markov model framework. Further improvements are expected because the approximate test risk bound minimization principle offers a flexible and rigorous framework to facilitate incorporation of new margin-based optimization criteria into hidden Markov model training.