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.

Finite-state decoding graphs integrate the decision trees, pronunciation model and language model for speech recognition into a unified representation of the search space. We explore discriminative training of the transition weights in the decoding graph in the context of large vocabulary speech recognition. In preliminary experiments on the RT-03 English Broadcast News evaluation set, the word error rate was reduced by about 5.7 % relative, from 23.0 % to 21.7%. We discuss how this method is particularly applicable to low-latency and low-resource applications such as real-time closed captioning of broadcast news and interactive speech-to-speech translation. Index Terms — Discriminative training, Finite-state decoding graph, Language model, Pronunciation model, Low-resource speech recognition.