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| 论文题目 |
Effective Acoustic Modeling for Pronunciation Quality Scoring of Strongly Accented Mandarin Speech |
| 论文题目（英文） |
Effective Acoustic Modeling for Pronunciation Quality Scoring of Strongly Accented Mandarin Speech |
| 作者 |
葛鹏裴 |
| 发表年度 |
2008 |
| 卷 |
E91-D |
| 期 |
10 |
| 页码 |
2385-2492 |
| 期刊名称 |
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| 摘要 |
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| 摘要_英文 |
In this paper we present our investigation into improving the performance of our computer-assisted language learning (CALL) system through exploiting the acoustic model and features within the speech recognition framework. First, to alleviate channel distortion, speaker-dependent cepstrum mean normalization (CMN) is adopted and the average correlation coefficient (average CC) between machine and expert scores is improved from 78.00% to 84.14%. Second, heteroscedastic linear discriminant analysis (HLDA) is adopted to enhance the discriminability of the acoustic model, which successfully increases the average CC from 84.14% to 84.62%. Additionally, HLDA causes the scoring accuracy to be more stable at various pronunciation proficiency levels, and thus leads to an increase in the speaker correct-rank rate from 85.59% to 90.99%. Finally, we use maximum a posteriori (MAP) estimation to tune the acoustic model to fit strongly accented test speech. As a result, the average CC is improved from 84.62% to 86.57%. These three novel techniques improve the accuracy of evaluating pronunciation quality.
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