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標題: 利用權重式Multiple Common Vector於中字彙之特定語者中文單音辨識
Using the Method of Weighted Multiple Common Vector to Recognize Isolated Mandarin Word for Speaker Dependent System
作者: 陳首鳴
Chen, Shou-Ming
關鍵字: Common Vector;共同向量;Principal Component Analysis;主成份分析
出版社: 應用數學系所
引用: [1] 王小川(2004), “語音訊號處理”,台北市:全華。 [2] 王國榮(2000),“Visual Basic 6.0 實戰講座 ”,台北巿:旗標。 [3] 李宗寶,黎自奮,楊茗惠(2003),用隱藏式馬可夫方法於頻域特徵之國語數字辨識,碩士論文,國立中興大學應用數學系,台中。 [4] 李宗寶,張國清(2005), “用K-means之動態時間軸校正法於國語數字之語音辨識”,碩士論文,國立中興大學應用數學研究所,台中。 [5] 李宗寶,林靖剛(2006), “利用Multiple Common Vector 於國語數字之語音辨識”,碩士論文,國立中興大學應用數學研究所,台中。 [6] 李宗寶,林子傑(2008),“利用Multiple Common Vector 及 Dynamic Time Warping於特定語者中文單音辨識” ,碩士論文,國立中興大學應用數學研究所,台中。 [7] 吳明哲,黃世陽(1998), “Visual Basic 6.0 中文版學習範本”,台北市:松崗。 [8] Angm, H.(1995), “Common vector obtained from linearly independent speech vectors by using LPC parameters,” graduation project, Elect. Electron. Eng. Dept., Osmangazi Univ., Eskisehir, Turkey. [9] Bing, X. and Yihe, S. (1996), “Research on ASIC for multi-speaker isolated word recognition”, ASIC, 2nd International Conference, pp. 135-137. [10] Bourouba, H., and Bedda, M. (2004), “HybridapproachDTW/HMMC for the recognition of the isolated Arabic words”, Information and Communication Technologies, 2004 International Conference on, pp. 481-482. [11] Chu, Myung-Kyung, and Sohn, Young-Sun (2001), “A User Friendly Interface Operated by the Improved DTW Method”, The 10th IEEE International Conference , vol. 3, pp. 1187-1190. [12] Gulmezoglu, M. B., Dzhafarov, V. and Barkana, A.(1999), “ A novel approach to isolated word recognition”, IEEE Trans. On Speech and Audio Processing, vol. 7. No. 6. [13] Gulmezoglu M. B., Dzhafarov, V. and Barkana, A. ,“The common vector approach and its relation to principal component analysis”, IEEE Trans. On Speech and Audio Processing, vol. 9. No. 6 [14] Harb, H., and Husseiny, A.H. (2000), “Isolated words recognition using neural networks”, The 7th IEEE International Conference on, 1, 17-20, pp. 349-351. [15] Keskin, M., Gulmezoglu, M. B., Parlaktuna, O. and Barkana, A. (1996), “Isolated word recognition by extracting personal differences,” in Proc. 6 th Int.Conf. Signal Processing Applications and Technology, Boston, MA , pp.1989-1992. [16] Rabiner, L.R. and Sambur, M.R.(1975), “An algorithm for determining the endpoints of isolated utterances”, The Bell System Technique Journal, Vol. 54, pp.297-315. [17] Rabiner, L.R. and Schmidt, C.E.(1980), “Application of Dynamic Time Warping to Connected Digital Recognition,” IEEE Transactions on Acoustics, Speech,and Signal Processing, Vol. 28, pp. 377-388. [18] Sakoe, H. and Chiba, S.(1978), “Dynamic Programming Optimization for Spoken Word Recognition,” IEEE Transactions on Acoustics, Speech, and Signal Processing, Vol. 26, pp. 43-49. Yucel, S.(1996), “Application of Gram-Schmidt orthogonalization method to speech recognition for different noise levels” graduation project, Elect. Electron. Eng. Dept., Osmangazi Univ., Eskisehir, Turkey.
本論文主要是探討200個國字單音的辨識,首先利用主成份分析與共同向量的關係來建構出語音模型,在辨識比對的部分,我們將音框分前後兩部份然後乘上不同的權重,考慮四個實驗因子:「音框數」、「特徵向量個數」、「音框的比例」及「音框的權重」,希望能找出在何種情況下200個字能具有不錯的鑑別度。而本論文的實驗結果,辨識200個字時,最高辨識率可達91.50 %。

This paper is to discuss the speech recognition of 200 isolated mandarin words. First, we use the relationship between principal component analysis and common vector to construct the speech model. Then we divide the total frame into two parts and give them different weighted of recognition. We consider four experimental factors in this paper: "the number of frame", "the number of eigenvector", "the ratio of the frame" and "the weighted of frame". We hope to find out which circumstances for the recognition of 200 words would be the best. And the maximum rate of recognition attains 91.50 % on the 200 words.
其他識別: U0005-0207200904154900
Appears in Collections:應用數學系所

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