A Novel Discriminative Method for Pronunciation Quality Assessment

Junbo ZHANG  Fuping PAN  Bin DONG  Qingwei ZHAO  Yonghong YAN  

IEICE TRANSACTIONS on Information and Systems   Vol.E96-D   No.5   pp.1145-1151
Publication Date: 2013/05/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E96.D.1145
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Speech and Hearing
pronunciation assessment,  automatic scoring,  distinctiveness training,  maximum entropy,  

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In this paper, we presented a novel method for automatic pronunciation quality assessment. Unlike the popular “Goodness of Pronunciation” (GOP) method, this method does not map the decoding confidence into pronunciation quality score, but differentiates the different pronunciation quality utterances directly. In this method, the student's utterance need to be decoded for two times. The first-time decoding was for getting the time points of each phone of the utterance by a forced alignment using a conventional trained acoustic model (AM). The second-time decoding was for differentiating the pronunciation quality for each triphone using a specially trained AM, where the triphones in different pronunciation qualities were trained as different units, and the model was trained in discriminative method to ensure the model has the best discrimination among the triphones whose names were same but pronunciation quality scores were different. The decoding network in the second-time decoding included different pronunciation quality triphones, so the phone-level scores can be obtained from the decoding result directly. The phone-level scores were combined into the sentence-level scores using maximum entropy criterion. The experimental results shows that the scoring performance was increased significantly compared to the GOP method, especially in sentence-level.