Unsupervised discriminative language model training

Abstract

As a final stage in an automatic speech recognition system, discriminative language modeling (DLM) aims to choose the most accurate word sequence among alternatives which are used as training examples. For supervised training, the manual transriptions of the spoken utterance are available. For unsupervised training this information is not present, therefore the level of accuracy of the training examples is not known. In this study we investigate methods to estimate these accuracies, and execute DLM training by using the perceptron algorithm adapted for structured prediction and reranking problems. The results show that with unsupervised training, it is possible to achieve improvements up to half of the gains obtained with the supervised case.