Discriminative Pronunciation Modeling Using the MPE Criterion
نویسندگان
چکیده
Introducing pronunciation models into decoding has been proven to be benefit to LVCSR. In this paper, a discriminative pronunciation modeling method is presented, within the framework of the Minimum Phone Error (MPE) training for HMM/GMM. In order to bring the pronunciation models into the MPE training, the auxiliary function is rewritten at word level and decomposes into two parts. One is for co-training the acoustic models, and the other is for discriminatively training the pronunciation models. On Mandarin conversational telephone speech recognition task, compared to the baseline using a canonical lexicon, the discriminative pronunciation models reduced the absolute Character Error Rate (CER) by 0.7% on LDC test set, and with the acoustic model co-training, 0.8% additional CER decrease had been achieved. key words: automatic speech recognition, pronunciation models, discriminative training, Mandarin conversational speech recognition
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Discriminative pronunciation modeling based on minimum phone error training
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ورودعنوان ژورنال:
- IEICE Transactions
دوره 98-D شماره
صفحات -
تاریخ انتشار 2015