Adapting acoustic models to new domains and conditions using untranscribed data

Asela Gunawardana and Alex Acero

Abstract

This paper investigates the unsupervised adaptation of an acoustic model to a domain with mismatched acoustic conditions. We use techniques borrowed from the unsupervised training literature to adapt an acoustic model trained on the Wall Street Journal corpus to the Aurora-2 domain, which is composed of read digit strings over a simulated noisy telephone channel. We show that it is possible to use untranscribed in-domain data to get significant performance improvements, even when it is severely mismatched to the acoustic model.

Details

Publication typeInproceedings
Published inInternational Conference on Speech Communication and Technology
PublisherInternational Speech Communication Association
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