An Approach to Designing Algorithms to Recognize Tones in Mandarin
碩士 === 國立交通大學 === 資訊科學系 === 87 === The Chinese language is a tonal language. The accuracy rate of a Chinese speech recognition system will be affected directly by the accuracy rate of its tone recognition algorithm. The recognition problem of tones deserves a closer look if we want to bui...
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ndltd-TW-087NCTU03940432016-07-11T04:13:35Z http://ndltd.ncl.edu.tw/handle/74593757839260533058 An Approach to Designing Algorithms to Recognize Tones in Mandarin 中文語音聲調辨識演算法 Yang Wen Horng 楊文宏 碩士 國立交通大學 資訊科學系 87 The Chinese language is a tonal language. The accuracy rate of a Chinese speech recognition system will be affected directly by the accuracy rate of its tone recognition algorithm. The recognition problem of tones deserves a closer look if we want to build a highly reliable Chinese speech recognition system. In this thesis, we studied the tone recognition problem in Mandarin. We have designed four features to distinguish the tones. We have also developed three major ways to classify tones. We have performed experiments to test the classification methods on large test samples (1390 syllables for each speaker with 19 speakers in total). The best classification method is the mixture one where we use the Euclidean distance on a multi-level decision tree. Its recognition rate is 89.35% for males and 80.90% for females of the mixture method. Individually, the recognition rate of the mixture method can reach higher than 90%. For on male the rate is 94.7%, 92.94%, 89.7% 92.4% for tone 1 to tone 4. Du Min Wen 杜敏文 1999 學位論文 ; thesis 47 en_US |
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碩士 === 國立交通大學 === 資訊科學系 === 87 === The Chinese language is a tonal language. The accuracy rate of a Chinese speech recognition system will be affected directly by the accuracy rate of its tone recognition algorithm. The recognition problem of tones deserves a closer look if we want to build a highly reliable Chinese speech recognition system.
In this thesis, we studied the tone recognition problem in Mandarin. We have designed four features to distinguish the tones. We have also developed three major ways to classify tones. We have performed experiments to test the classification methods on large test samples (1390 syllables for each speaker with 19 speakers in total). The best classification method is the mixture one where we use the Euclidean distance on a multi-level decision tree. Its recognition rate is 89.35% for males and 80.90% for females of the mixture method. Individually, the recognition rate of the mixture method can reach higher than 90%. For on male the rate is 94.7%, 92.94%, 89.7% 92.4% for tone 1 to tone 4.
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Du Min Wen |
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Du Min Wen Yang Wen Horng 楊文宏 |
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Yang Wen Horng 楊文宏 |
spellingShingle |
Yang Wen Horng 楊文宏 An Approach to Designing Algorithms to Recognize Tones in Mandarin |
author_sort |
Yang Wen Horng |
title |
An Approach to Designing Algorithms to Recognize Tones in Mandarin |
title_short |
An Approach to Designing Algorithms to Recognize Tones in Mandarin |
title_full |
An Approach to Designing Algorithms to Recognize Tones in Mandarin |
title_fullStr |
An Approach to Designing Algorithms to Recognize Tones in Mandarin |
title_full_unstemmed |
An Approach to Designing Algorithms to Recognize Tones in Mandarin |
title_sort |
approach to designing algorithms to recognize tones in mandarin |
publishDate |
1999 |
url |
http://ndltd.ncl.edu.tw/handle/74593757839260533058 |
work_keys_str_mv |
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