In this paper we describe how discriminative training can be applied to language models for speech recognition. Language models are important to guide the speech recognizer, particularly in compensating for mistakes in acoustic decoding. A frequently used measure of the quality of language models is the perplexity; however, what is more important for accurate decoding is not necessarily having the maximum likelihood, but rather the best separation of the correct string from the competing, acoustically confusible hypotheses. Discriminative training can help to improve language models for the purpose of speech recognition by improving the separation of the correct hypothesis from the competing hypotheses. We describe the algorithm and demonstrate modest improvements in word and sentence error rates on the DARPA Communicator task. 1.

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. I.

We present a novel confidence- and marginbased discriminative training approach for model adaptation of a hidden Markov model (HMM) based handwriting recognition system to handle different handwriting styles and their variations. Most current approaches are maximum-likelihood (ML) trained HMM systems and try to adapt their models to different writing styles using writer adaptive training, unsupervised clustering, or additional writer specific data. Here, discriminative training based on the maximum mutual information (MMI) and minimum phone error (MPE) criteria are used to train writer independent handwriting models. For model adaptation during decoding, an unsupervised confidence-based discriminative training on a word and frame level within a two-pass decoding process is proposed. The proposed methods are evaluated for closedvocabulary isolated handwritten word recognition on the IFN/ENIT Arabic handwriting database, where the word-error-rate is decreased by 33 % relative compared to a ML trained baseline system. On the largevocabulary line recognition task of the IAM English handwriting database, the word-error-rate is decreased by 25 % relative.

This paper gives an overview of discriminative training as it pertains to the speech recognition problem. The basic theory of discriminative training will be discussed and an explanation of maximum mutual information (MMI) given. Common problems inherent to discriminative training will be explored as well as practicalities associated with implementing discriminative training for large vocabulary recognition. Alternatives to the MMI objective function such as minimum word error (MWE) and minimum phone error (MPE) will be discussed. The application of discriminative techniques for adaptation will be described. Finally, possible future avenues of research will be given. 1.