關鍵字查詢 | 類別:會議論文 | | 關鍵字:Distributed fuzzy rules for preprocessing of speech segmentation with genetic algorithm

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序號 學年期 教師動態
1 85/2 電機系 賴友仁 教授 會議論文 發佈 Distributed fuzzy rules for preprocessing of speech segmentation with genetic algorithm , [85-2] :Distributed fuzzy rules for preprocessing of speech segmentation with genetic algorithm會議論文Distributed fuzzy rules for preprocessing of speech segmentation with genetic algorithm利用遺傳演算法以分散式模糊規則為基礎的語音切割的前處理Hsieh, Ching-tang; Lai, Eugene; Wang, You-chuang淡江大學電機工程學系IEEE Neural Networks Council; Artificial Intelligence Research Institute of the CSICFuzzy Systems, 1997., Proceedings of the Sixth IEEE International Conference on vol.1, pp.427-431Most of the speech segmentation works are based on the thresholds of parameters to segment the speech data into phonemic units or syllabic units. In this paper, we formulate the threshold decision as a clustering problem. Feature parameters extracted from the analysis frame are clustered into three types: silence, consonants, and vowels. Distributed fuzzy rules which have been used in clustering the numerical data are used for this task. The distributed fuzzy rules, which do not need many training data, have good performance in clustering problems an
2 85/2 電機系 謝景棠 教授 會議論文 發佈 Distributed fuzzy rules for preprocessing of speech segmentation with genetic algorithm , [85-2] :Distributed fuzzy rules for preprocessing of speech segmentation with genetic algorithm會議論文Distributed fuzzy rules for preprocessing of speech segmentation with genetic algorithm利用遺傳演算法以分散式模糊規則為基礎的語音切割的前處理Hsieh, Ching-tang; Lai, Eugene; Wang, You-chuang淡江大學電機工程學系IEEE Neural Networks Council; Artificial Intelligence Research Institute of the CSICFuzzy Systems, 1997., Proceedings of the Sixth IEEE International Conference on vol.1, pp.427-431Most of the speech segmentation works are based on the thresholds of parameters to segment the speech data into phonemic units or syllabic units. In this paper, we formulate the threshold decision as a clustering problem. Feature parameters extracted from the analysis frame are clustered into three types: silence, consonants, and vowels. Distributed fuzzy rules which have been used in clustering the numerical data are used for this task. The distributed fuzzy rules, which do not need many training data, have good performance in clustering problems an
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