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