Bayesian large margin hidden Markov models for speech recognition

This paper presents a Bayesian learning approach to large margin classifier for hidden Markov model (HMM) based speech recognition. We build the Bayesian large margin HMMs (BLM-HMMs) and improve the model generalization for handling unknown test environments. Using BLM-HMMs, the variational Bayesian HMM parameters are estimated by maximizing lower bound of a marginal likelihood over the uncertainties of HMM parameters. The Bayesian large margin estimation is performed with frame selection mechanism, and is illustrated to meet the objective of support vector machines, i.e. maximal class margin and minimal training errors. The new objective function is not only interpreted as a discriminative criterion, but also feasible to deal with model selection and adaptive training. Experiments on phone recognition show that BLM-HMMs perform better than other generative and discriminative models.