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    National Tsing Hua University Institutional Repository > 理學院 > 統計學研究所 > 會議論文  >  A NEW HYBRID DURATION HIDDEN MARKOV MODEL WITH APPLICATION TO LARGE VOCABULARY TAIWANESE (MIN-NAN) WORD RECOGNITION


    Please use this identifier to cite or link to this item: http://nthur.lib.nthu.edu.tw/dspace/handle/987654321/54066


    Title: A NEW HYBRID DURATION HIDDEN MARKOV MODEL WITH APPLICATION TO LARGE VOCABULARY TAIWANESE (MIN-NAN) WORD RECOGNITION
    Authors: CHIANG Yuang-Chin;FANG Ren-Zhou;HSIEH Wen-Ping;LYU Ren-Yuan
    教師: 江永進
    Date: 1998
    Relation: ISCSLP1998,Singapore
    Keywords: NEW HYBRID DURATION HIDDEN MARKOV MODEL
    LARGE VOCABULARY TAIWANESE (MIN-NAN) WORD RECOGNITION
    Abstract: A new hybrid duration Hidden Markov Model (hdHMM), which combines the ideas of both the infinite duration models and finite duration models, is proposed here and applied to a large vocabulary Taiwanese speech recognition task. Such a model not only has better state duration distribution than the traditional left-to-right HMM but also is more
    computational efficient than the finite-duration HMM. The experiment was performed on a large vocabulary Taiwanese (Min-nan) multi-syllabic word recognition. For the speaker dependent case, the best word error rate achieved here is 7.9%. Since this paper is also one of the first papers on the speech recognition of Taiwanese speech, some basic facts about Taiwanese phonetics is also briefly introduced.
    URI: http://www.isca-speech.org/archive_open/archive_papers/iscslp1998/ASR-A5.pdf
    http://nthur.lib.nthu.edu.tw/dspace/handle/987654321/54066
    Appears in Collections:[統計學研究所] 會議論文

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