Unsupervised acoustic and language model training with small amounts of labelled data

Abstract

We measure the effects of a weak language model, estimated from as little as 100k words of text, on unsupervised acoustic model training and then explore the best method of using word confidences to estimate n-gram counts for unsupervised language model training. Even with 100k words of text and 10 hours of training data, unsupervised acoustic modeling is robust, with 50% of the gain recovered when compared to supervised training. For language model training, multiplying the word confidences together to get a weighted count produces the best reduction in WER by 2% over the baseline language model and 0.5% absolute over using unweighted transcripts. Oracle experiments show that a larger gain is possible, but better confidence estimation techniques are needed to identify correct n-grams.