Multiple-Pronunciation Lexical Modeling Based on Phoneme Confusion Matrix for Dysarthric Speech Recognition
نویسندگان
چکیده
In this paper, we propose speaker-dependent multiple-pronunciation lexical modeling for improving the performance of dysarthric automatic speech recognition (ASR). For each dysarthric speaker, a phoneme confusion matrix is first constructed from the results of phoneme recognition. Then, pronunciation variation rules are extracted by investigating the phoneme confusion matrix, and they are incorporated into a baseline lexicon to construct a multiplepronunciation lexicon. It is shown from dysarthric ASR experiments that an ASR system using the proposed speaker-dependent multiple-pronunciation lexicon relatively reduces the average word error rate by 5.06% compared to that using a group-dependent multiple pronunciation lexicon.
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