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Developing a practical speech recognizer for a low resource language ischallenging, not only because of the (potentially unknown) properties of thelanguage, but also because test data may not be from the same domain as theavailable training data. In this paper, we focus on the latter challenge, i.e.domain mismatch, for systems trained using a sequence-based criterion. Wedemonstrate the effectiveness of using a pre-trained English recognizer, whichis robust to such mismatched conditions, as a domain normalizing featureextractor on a low resource language. In our example, we use TurkishConversational Speech and Broadcast News data. This enables rapid developmentof speech recognizers for new languages which can easily adapt to any domain.Testing in various cross-domain scenarios, we achieve relative improvements ofaround 25% in phoneme error rate, with improvements being around 50% for somedomains.

Domain Robust Feature Extraction for Rapid Low Resource ASR Development