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Unsupervised domain adaptation with multiple acoustic models

We investigate the problem of adapting a recognition system with multiple acoustic models to a new domain in unsupervised mode. We compare maximum likelihood and discriminative approaches for unsupervised domain adaptation. Different adaptation data selection methods and adaptation strategies are investigated, using a baseline meeting recognition system and adaptation data from a congressional committee web site. Experiments show that one should avoid adapting all acoustic models to the same recognition output, and that ASR confidence estimates improve results when used for rejecting low-quality ASR output. The results show 8% relative overall improvement from unsupervised adaptation.