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標題: 利用混合式及PCA之辨識法於特定語者中文單音辨識
Using the mixed data and PCA to recognition isolated mandarin word for speaker-dependent system
作者: 陳少宇
Chen, Shao-Yu
關鍵字: K-means;k-means;PCA;cepstrum coefficient;δ- cepstrum.;主成份分析;倒頻譜參數;差倒頻譜參數
出版社: 應用數學系所
引用: [1] 李宗寶,張國清。“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,2005。 [2] 李宗寶,吳宗憲。“探討K-means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。 [3] 李宗寶,王奕凱。“利用混合式之辨識法於國語數字”。碩士論文,國立中興大學應用數學研究所,台中,2006。 [4] 王小川。“語音訊號處理”。台北市:全華,2004。 [5] Gulmezoglu, M. B., Dzhafarov, Vakif and Barkana, Atalay “ A novel approach to isolated word recognition”, IEEE Trans. On Speech and Audio Processing, vol. 7. No. 6, 1999. [6] Bilginer Gulmezoglu,M., Dzhafarov, Vakif and Barkana, Atalay ,“The common vector approach and its relation to principal component analysis”, IEEE Trans. On Speech and Audio Processing, vol. 9, No. 6. [7] Keskin,M.,Gulmezoglu,M.B., Parlaktuna,O. and Barkana,A. ,“Isolated word recognition by extracting personal differences”, in Proc. 6 th Int.Conf. Signal Processing Applications and Technology, Boston, MA, pp.1989-1992, 1996. [8] Yucel, S., “Application of Gram-Schmidt orthogonalization method to speech recognition for different noise levels”, graduation project, Elect. Electron. Eng. Dept., Osmangazi Univ., Eskisehir, Turkey, 1996. [9] Angm, H.,“Common vector obtained from linearly independent speech vectors by using LPC parameters”, graduation project, Elect. Electron. Eng. Dept., Osmangazi Univ., Eskisehir, Turkey, 1995. [10] Bogert, B. P., Healy, W. J. R., and Tukey, J. W.,“ The frequency analysis of time series for echoes : cepstrum , pseudo-autocovariance , cross-cepstrumand saphe cracking ”,in Proc. Symp. Time Series Analysis.New York: Wiley, 1963, pp.209-243. [11] Bing, X., Yihe, S., Research on ASIC for multi-speaker isolated word reconition, ASIC, 2nd International Conference, 21-24, 135-137,1996. [12]Tiemey, J., “A study of LPC analysis of speech inadditive noise ”,IEEE Trans. Acoust. Speech Signal Process. ASSP-28 (4) pp. 389-397, 1980. [13] Atal, B. S., Hanauer, S. L. “ Speech analysis and synthesis by linear prediction of the speech wave ”, J. Acoust. Soc. Am. 50 pp. 637-655, 1971.
論文中所使用到的辨識方法為「K-means之混合式辨識法」及「K-means and 主成分分析法之混合式辨識法」,比較訓練語音之1.向量維度、2.K-means分群數,3.特徵向量個數等因子對待測語音在辨識上的影響及結果。

The thesis is to investigate the speech recognition of 337 isolated mandarin words for the specific speaker. The study will start from 50 isolated mandarin words, and gradually increase to 337 words. We hope that the recognition rate would be at least 80% when the number of mandarin words increases.
The recognition methods we're using in the thesis are “Hybrid method with K-means” and “Hybrid method with K-means and PCA”. Three factors are considered such as dimension of speech extraction, the number of cluster and the number of eigenvector.
The result of the experiment is operated by speech extraction that is the combination of parameters between cepstrum coefficient and δ- cepstrum. The recognition rate of experiment may highly result in 95.71% under 50 isolated mandarin words. However, the rate of recognition may attain at least 84.46% up when the words increase to 130. Finally some suggestions are given to improve the recognition rate for the future work
其他識別: U0005-2306200821534500
Appears in Collections:應用數學系所

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