Abstract
Utterance verification is used in variable vocabulary word recognition to reject the word that does not belong to in-vocabulary word or does not belong to correctly recognized word. Utterance verification is very important to design a user-friendly speech recognition system. We propose a new utterance verification algorithm that, with no-training required, is based on the minimum verification error. First, using PBW (Phonetically Balanced Words) DB (445 words), we generate no-training antiphoneme models. Then, for OOV (Out-Of-Vocabulary) rejection, a new confidence measure which uses the likelihood between phoneme model and anti-phoneme model is designed. Using our proposed anti-phoneme model and confidence measure, we achieve significant performance improvement; CA (Correctly Accept for In-Vocabulary) is about 89%, and CR (Correctly Reject for OOV) is about 90%, improving about 15-21% in ERR (Error Reduction Rate), in the vocabulary-independent case.
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