Adapting acoustic models to new domains and conditions using untranscribed data
- Asela Gunawardana ,
- Alex Acero
International Conference on Speech Communication and Technology |
Published by International Speech Communication Association
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.
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