English  |  正體中文  |  简体中文  |  全文筆數/總筆數 : 61881/94641 (65%)
造訪人次 : 1637598      線上人數 : 10
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
搜尋範圍 查詢小技巧:
  • 您可在西文檢索詞彙前後加上"雙引號",以獲取較精準的檢索結果
  • 若欲以作者姓名搜尋,建議至進階搜尋限定作者欄位,可獲得較完整資料
  • 進階搜尋
    請使用永久網址來引用或連結此文件: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/75109

    題名: A New Method for Measuring Similarity Between Two GMMs
    作者: Ting, Chuan-Wei;Chen, Li-Ching;He, Chih-Liang
    貢獻者: 淡江大學統計學系
    關鍵詞: Similarity between two GMMs;Hypothesis testing;HMM
    日期: 2011-06
    上傳時間: 2012-03-13 01:41:25 (UTC+8)
    出版者: Toroku: ICIC International
    摘要: This study presents a new method for measuring similarity between two Gaussian mixture models (GMMs) to discover how to compensate for variations in the topology of adaptive hidden Markov models (HMM). The aims of the proposed scheme is to determine whether a new state topology with different variations should be added to existing acoustic models in response to the addition of training data. The testing of two Gaussian densities is frequently used in the sharing of parameters between Gaussian components of HMM. In this work, we extend such hypothesis to measure similarities between two GMMs and estimate the statistic from the proposed test through the summation of two gamma distributions. A new HMM topology is automatically generated according to a level of significance. The dataset-dependent characteristics and variations are handled with an adaptive HMM topology. Experiments on speech recognition tasks show that the proposed testing scheme performs significantly better than the standard HMM with a comparable size of parameters.
    關聯: ICIC Express Letters 5(6), pp.1839-1844
    顯示於類別:[統計學系暨研究所] 期刊論文


    檔案 描述 大小格式瀏覽次數
    1881-803X_5(6)p1839-1844.pdf4805KbAdobe PDF4檢視/開啟



    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - 回饋