標題: 結合貝氏估計法的修正最大概似法在語音辨識的應用
Maximum Likelihood Method Associated with the Bayes Approach in Audio Signal Recognition
作者: 黃依賢
Huang, E-Hsien
王秀瑛
Wang, Hsiu-Ying
統計學研究所
關鍵字: 貝氏估計;最大概似估計;音訊辨識;高斯混合模型;bayes estimate;maximum likelihood method;audio signal recognition;gaussian mixture model
公開日期: 2008
摘要: 近代聲音辨識系統普遍建立在Gaussian mixture model 或Hidden Markov Model.最大概似法事最常被用來估計這些模型,而另一種估計法則是採用貝氏估計,在本研究中,我們想設計一個結合貝氏估計與最大概似估計的新方法來提升採用Gaussian mixture model的聲音辨識效果。.
Modern speech recognition systems are generally based on the Gaussian mixture model or Hidden Markov Model. The maximum likelihood method is a popular approach to derive the parameter estimation in these models. An alternative solution is to adopt the Bayes approach for the parameter estimation. In this study, we propose a new method which can combine the results of the maximum likelihood and Bayes approaches to provide more accurate estimators in the Gaussian mixture model for audio signal recognition.
URI: http://140.113.39.130/cdrfb3/record/nctu/#GT079626521
http://hdl.handle.net/11536/42682
Appears in Collections:Thesis