| A fuzzy-clustering analysis based phonetic tied-mixture HMM |
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| 作者姓名： | XU Xianghua ZHU Jie GUO Qiang |
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| 作者单位： | [1]School of Control Science and Engineering, Shandong University Ji＇nan 250061 [2]Department of Electronic Engineering, Shanghai Jiaotong University Shanghai 200030 |
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| 基金项目： | The work was supported by the Science and Technology Committee of Shanghai (01JC14033). |
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| 摘 要： | To efficiently decrease the size of parameters and improve the robustness of parameters training, a fuzzy clustering based phonetic tied-mixture model, FPTM, is presented. The Gaussian codebook of FPTM is synthesized from Gaussian components belonging to the same root node in phonetic decision tree. Fuzzy clustering method is further used for FPTM covariance sharing. Experimental results show that compared with the conventional PTM with approximately the same parameters size, FPTM decrease the size of Gaussian weights by 77.59% and increases word accuracy by 7.92%, which proves Gaussian fuzzy clustering is efficient. Compared with FPTM, covariance-shared FPTM decreases word error rate by 1.14% , which proves the combined fuzzy clustering for both Gaussian and covariance is superior to Gaussian fuzzy clustering alone.
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| 关 键 词： | 失真-偏聚分析 语音矩阵 HMM 鲁棒性 |
| 收稿时间： | 2004-01-11 |
| 修稿时间： | 2004-01-112005-02-22 |
A fuzzy-clustering analysis based phonetic tied-mixture HMM |
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| Authors: | XU;Xianghua;ZHU;Jie;GUO;Qiang |
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| Abstract: | To efficiently decrease the size of parameters and improve the robustness of parameters training, a fuzzy clustering based phonetic tied-mixture model, FPTM, is presented. The Gaussian codebook of FPTM is synthesized from Gaussian components belonging to the same root node in phonetic decision tree. Fuzzy clustering method is further used for FPTM covariance sharing. Experimental results show that compared with the conventional PTM with approximately the same parameters size, FPTM decrease the size of Gaussian weights by 77.59% and increases word accuracy by 7.92%, which proves Gaussian fuzzy clustering is efficient. Compared with FPTM, covariance-shared FPTM decreases word error rate by 1.14% , which proves the combined fuzzy clustering for both Gaussian and covariance is superior to Gaussian fuzzy clustering alone. |
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