HMM based phoneme classification using several types of segmentation algorithms
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概要
- 論文の詳細を見る
We have attempted to automatically classify the Japanese 23 phonemes (extracted from the isolated mono-syllables) hierarchically by using Hidden Markov models (HMM). For extracting the phonemes we have considered four different types of segmentation algorithms, viz., MDS, MMDS, MLS, and LB. Additionally we have imposed some kind of linear weighting on the discrete probability density function of HMM. The results show that classification efficiency is comparatively better for MMDS and weighting improves the efficiency by 21% for the training data and by 8.5% for the testing data.
- 社団法人電子情報通信学会の論文
- 1996-07-26
著者
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Tsuzuki Shinji
Inst. Low Temp. Sci. Hokkaido Univ.
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Podder S.K.
Ehime University, Faculty of engineering, Department of Electrical and Electronic Engineering
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Tazaki S.
Ehime University, Faculty of engineering, Department of Electrical and Electronic Engineering
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Tsuzuki S.
Ehime University, Faculty of engineering, Department of Electrical and Electronic Engineering
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Yamada Y.
Ehime University, Faculty of engineering, Department of Electrical and Electronic Engineering
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Podder S.k.
Ehime University Faculty Of Engineering Department Of Electrical And Electronic Engineering
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