Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/17509
標題: 利用Multiple Common Vector 於國語數字之語音辨識
Using the Method of Multiple Common Vector to Speech Recognition of Mandarin digits
作者: 林靖剛
Lin, Ching-Kang
關鍵字: 共同向量法;common vector;主成份分析;PCA
出版社: 應用數學系所
引用: [1] M. B. Gulmezoglu, Vakif Dzhafarov, and Atalay Barkana, “ A novel approach to isolated word recognition,” IEEE Trans. On Speech and Audio Processing, vol. 7. No. 6, 1999. [2] M. Bilginer Gulmezoglu, Vakif Dzhafarov, and Atalay Barkana ,“The common vector approach and its relation to principal component analysis” IEEE Trans. On Speech and Audio Processing, vol. 9. No. 6 [3] M. Keskin, M. B. Gulmezoglu, O. Parlaktuna, and A. Barkana, “Isolated word recognition by extracting personal differences,” in Proc. 6 th Int.Conf. Signal Processing Applications and Technology, Boston, MA , pp.1989-1992, 1996. [4] S. Yucel, “Application of Gram-Schmidt orthogonalization method to speech recognition for different noise levels” graduation project, Elect. Electron. Eng. Dept., Osmangazi Univ., Eskisehir, Turkey, 1996. [5] H. Angm, “Common vector obtained from linearly independent speech vectors by using LPC parameters,” graduation project, Elect. Electron. Eng. Dept., Osmangazi Univ., Eskisehir, Turkey, 1995. [6] 李宗寶,吳宗憲。” 探討Kmean之共同向量法應用於國語數字辨識”。碩士論文,國立中興大學應用數學研究所,台中,2005。 [7] 陳志堅,黃銘崇。”不特定語者語詞辨識系統之特徵設計” 。碩士論文,國立中山大學電機工程研究所,台中,2001。 [8] 李宗寶,林峰樣。” 探討共同向量法應用於國語數字辨識”。碩士論文,國立中興大學應用數學研究所,台中,2004。 [9] 王小川。”語音訊號處理”。台北市:全華,2004。
摘要: 
本論文主要是利用主成份分析與共同向量的關係來建構語音模型及辨識比對,目的是希望能解決單獨使用共同向量法時樣本數不能過多的限制,並且找出在語音特徵維度固定的情況之下,取多少個特徵向量會有最佳的辨識率。對於採用不同的特徵向量取法是否皆能有良好的辨識率。在收集的語料為有限的情況下,希望能建構出最佳的語音模型。
URI: http://hdl.handle.net/11455/17509
其他識別: U0005-0707200613215900
Appears in Collections:應用數學系所

Show full item record
 

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.