Discriminative Training of Acoustic Models for Mispronunciation Detection and Diagnosis of Non-native English
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| Publicado en: | ProQuest Dissertations and Theses (2015) |
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| Acceso en línea: | Citation/Abstract Full Text - PDF |
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| 001 | 1846478052 | ||
| 003 | UK-CbPIL | ||
| 020 | |a 978-1-369-41027-3 | ||
| 035 | |a 1846478052 | ||
| 045 | 0 | |b d20150101 | |
| 084 | |a 66569 |2 nlm | ||
| 100 | 1 | |a Qian, Xiaojun | |
| 245 | 1 | |a Discriminative Training of Acoustic Models for Mispronunciation Detection and Diagnosis of Non-native English | |
| 260 | |b ProQuest Dissertations & Theses |c 2015 | ||
| 513 | |a Dissertation/Thesis | ||
| 520 | 3 | |a This thesis applies discriminative training techniques to improve the acoustic modeling in mispronunciation detection and diagnosis (MD&D) for computer-aided pronunciation training. Discriminative training of generative models improves classification performance by bringing in competing classes and optimizes a task-relevant evaluation criterion to tune the decision boundaries, as is done in discriminative models by nature. This work formulates and optimizes discriminative training criteria for generative GMM-HMMs in two broad frameworks of MD&D The first framework explicitly models the phonetic error patterns from a labelled non-native speech corpus and populates the recognition network with the extracted and predicted error patterns. Discriminative training of GMM-HMMs by minimizing the expected full-sequence word-level errors brings down the word-level error by 16% relative. Nevertheless, explicit error pattern modeling suffers from missing error patterns and inclusion of rare and idiosyncratic ones. In addition, a balance has to be stroke between under-generation and over-generation of error patterns. The second and recently-proposed framework seeks to abandon explicit error pattern modeling by instantiating a set of anti-phones and a filler model with GMM-HMMs, and crafts general phone error detection and diagnosis networks that encompasses all possible errors. This design renders explicit error pattern modeling unnecessary. In the two-pass framework, discriminative training of GMM-HMMs by minimizing the full-sequence phone-level errors lowers the phone-level error by 40% relative. Visualization of the GMM parameters shows that discriminative training effectively separates the canonical phones and their anti-phones. | |
| 653 | |a Electrical engineering | ||
| 773 | 0 | |t ProQuest Dissertations and Theses |g (2015) | |
| 786 | 0 | |d ProQuest |t ProQuest Dissertations & Theses Global | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/1846478052/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/1846478052/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |