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題名：Modeling Pronunciation Variation for Bi-Lingual Mandarin/Taiwanese Speech Recognition
書刊名：International Journal of Computational Linguistics & Chinese Language Processing
作者：Lyu, Dau-cheng／Lyu, Ren-yuan／Chiang, Yuang-chin／Hsu, Chun-nan
出版日期：2005
卷期：10:3
頁次：頁363-380
主題關鍵詞：Bi-lingual；One-pass ASR；Pronunciation modeling
原始連結：連回原系統網址new window
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  • 共同引用共同引用:3
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In this paper, a bi-lingual large vocaburary speech recognition experiment based on the idea of modeling pronunciation variations is described. The two languages under study are Mandarin Chinese and Taiwanese (Min-nan). These two languages are basically mutually unintelligible, and they have many words with the same Chinese characters and the same meanings, although they are pronounced differently. Observing the bi-lingual corpus, we found five types of pronunciation variations for Chinese characters. A one-pass, three-layer recognizer was developed that includes a combination of bi-lingual acoustic models, an integrated pronunciation model, and a tree-structure based searching net. The recognizer’s performance was evaluated under three different pronunciation models. The results showed that the character error rate with integrated pronunciation models was better than that with pronunciation models, using either the knowledge-based or the data-driven approach. The relative frequency ratio was also used as a measure to choose the best number of pronunciation variations for each Chinese character. Finally, the best character error rates in Mandarin and Taiwanese testing sets were found to be 16.2% and 15.0%, respectively, when the average number of pronunciations for one Chinese character was 3.9.
期刊論文
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