Discriminative Models for Speech Recognition

Abstract

The vast majority of automatic speech recognition systems use hidden Markov models (HMMs) as the underlying acoustic model. Initially these models were trained based on the maximum likelihood criterion. Significant performance gains have been obtained by using discriminative training criteria, such as maximum mutual information and minimum phone error. However, the underlying acoustic model is still generative, with the associated constraints on the state and transition probability distributions, and classification is based on Bayes' decision rule. Recently, there has been interest in examining discriminative, or direct, models for speech recognition. This paper briefly reviews the forms of discriminative models that have been investigated. These include maximum entropy Markov models, hidden conditional random fields and conditional augmented models. The relationships between the various models and issues with applying them to large vocabulary continuous speech recognition will be discussed.