Sixth European Conference on Speech Communication and Technology
Using a Large-Vocabulary, Continuous Speech Recognizer in a high-Volume application such as a commercial transcription service presents a different set of challenges and constraints than in a laboratory setting. We examine these differences with regard to acoustic model adaptation and find serious shortcomings in both the supervised and unsupervised approaches. We then examine a new method, semi-supervised adaptation, which overcomes the limitations of the other methods and can reduce recognition error rates by as much as 15% more than the reduction obtained through unsupervised adaptation.
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Bibliographic reference. Wightman, Colin W. / Harder, Ted A. (1999): "Semi-supervised adaptation of acoustic models for large-volume dictation", In EUROSPEECH'99, 1371-1374.