Unsupervised Languagemodel Adaptation for Meeting Recognition

Abstract

We present an application of unsupervised language model (ML) adaptation to meeting recognition, in a scenario where sequences of multiparty meetings on related topics are to be recognized, but no prior in-domain data for LM training is available. The recognizer LMs are adapted according to the recognition output on temporally preceding meetings, either in speaker-dependent or speaker-independent mode. Model adaptation is carried out by interpolating the n-gram probabilities of a large generic LM with those of a small LM estimated from adaptation data, and minimizing perplexity on the automatic transcripts of a separate meeting set, also previously recognized. The adapted LMs yield about 5.9% relative reduction in word error compared to the baseline. This improvement is about half of what can be achieved with supervised adaptation, i.e. using human-generated speech transcripts.

DOI: 10.1109/ICASSP.2007.367191

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Cite this paper

@article{Tr2007UnsupervisedLA, title={Unsupervised Languagemodel Adaptation for Meeting Recognition}, author={G{\"{o}khan T{\"{u}r and Andreas Stolcke}, journal={2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07}, year={2007}, volume={4}, pages={IV-173-IV-176} }