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ICASSP
2009
IEEE

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

10 years 5 months ago
Unsupervised acoustic and language model training with small amounts of labelled data
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.
Scott Novotney, Richard M. Schwartz, Jeff Ma
Added 21 May 2010
Updated 21 May 2010
Type Conference
Year 2009
Where ICASSP
Authors Scott Novotney, Richard M. Schwartz, Jeff Ma
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