n South African Computer Journal - Unsupervised adaptation of statistical language models for speech recognition : research article

Volume 2003, Issue 30
  • ISSN : 1015-7999
  • E-ISSN: 2313-7835



It has been demonstrated repeatedly that the acoustic models of a speaker-independent speech recognition system can benefit substantially from the application of unsupervised adaptation methods as a means of speaker enrollment. Unsupervised adaptation has however not yet been applied to the statistical language model component of the recognition system. We investigate two techniques with which a first-pass recognition transcription is used to adapt the parameters of the n-gram language model that is used in the recognition search. It is found that best results are achieved when both methods are employed in conjunction with each other. The performance of the adaptation methods were determined experimentally by application to the transcription of a set of lecture speeches. Improvements both in terms of language model perplexity as well as recognition word error-rate were achieved.

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