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TRIPS employs user-independent speech recognition using Sphinx-II, a general-purpose speech recognizer from CMU. We use Sphinx' generic acoustic models and create our own language models based on the task domain we are implementing. The result of recognition is a set of word hypotheses which is sent to the parser.
Building a language model for a new domain requires a large corpus in the domain, which can be time-consuming and expensive. We have successfully used two techniques to circumvent this. First, we generate an artificial corpus based on a hand-coded, task specific CFG. Second, we use a class-based approach to "port" out-of-domain corpora to our new domain. This allows us to effectively re-use other problem-solving dialogues that we have previously collected.