Trigger-Based Language Modeling Using a Loss-Sensitive Perceptron Algorithm

Discriminative language models using n-gram features have been shown to be effective in reducing speech recognition word error rates. In this paper we describe a method for incorporating discourselevel triggers into a discriminative language model. Triggers are features identifying re-occurrence of words within a conversation. We introduce triggers that are specific to particular unigrams and bigrams, as well as “back off ” trigger features that allow generalizations to be made across different unigrams. We train our model using a new loss-sensitive variant of the perceptron algorithm that makes effective use of information from multiple hypotheses in an n-best list. We train and test on the Switchboard data set and show a 0.5 absolute reduction in WER over a baseline discriminative model which uses n-gram features alone, and a 1.5 absolute reduction in WER over the baseline recognizer. Index Terms — Perceptrons, Speech recognition, Natural languages 1