Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/18757
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dc.contributor李宗寶zh_TW
dc.contributor.author劉懿宸zh_TW
dc.contributor.authorLiu, Yi-Chenen_US
dc.contributor.other統計學研究所zh_TW
dc.date2013en_US
dc.date.accessioned2014-06-06T07:04:32Z-
dc.date.available2014-06-06T07:04:32Z-
dc.identifierU0005-0907201317195100en_US
dc.identifier.citation1 Bayrakceken M.K., Cay M.A. and Barkana A., (2007),"Word Spotting Using Common Vector Approach", Signal Processing and Communications Applications, IEEE 15th , pp. 1 - 4. 2 Cevikalp H., Neamtu M., Barkana A., (2007),"The Kernel Common Vector Method: A Novel Nonlinear Subspace Classifier for Pattern Recognition", Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Trans. on Speech and Audio Processing, Vol. 37, Issue: 4 , pp. 937 - 951 3 Edizkan R., Gu ̈lmezog ̌lu M. B., Ergin S. and Barkana A., (2005),"Improvements on common vector approach for multi class problems", In 13th European Signal Processing Conference, Antalya, Turkiye. 4 Ergin S. and Gu ̈lmezog ̌lu M.B., (2007),"Face recognition based on face partitions using Common Vector Approach", Communications, Control and Signal Processing, ISCCSP 2008. 3rd International Symposium on, pp. 624-628. 5 Gu ̈lmezog ̌lu M. B., Dzhafarov V., Keskin M., and Barkana A., (1999),"A Novel Approach to Isolated Word Recognition", IEEE Trans. On Speech and Audio Processing, Vol. 7, no. 6, pp. 620-628. 6 Gu ̈lmezog ̌lu M. B., Dzhafarov V. and Barkana A., (2001),"The Common Vector Approach and Its Relation to Principal Component Analysis", IEEE Trans. On Speech and Audio Processing, Vol. 9, no. 6, pp. 655-662. 7 Fukunaga K. and Koonz W. L., (1970),"Application of the Karhuenn-Loeve expansion to feature selection and ordering", IEEE Trans. Comput., Vol. C-19, no. 4,pp. 311-318. 8 Iijima T., Genchi H., Mori K., (1973),"A theory of character recognition by pattern matching method", The Proc. of the 1st International Joint Conference on Pattern Recognition, Washington, DC, pp. 50–56. 9 Keser S. and Edizkan R., (2009)," Phonem-based isolated Turkish word recognition with subspace classifier", Signal Processing and Communications Applications Conference, IEEE 17th, pp. 93 - 96 10 Lakshmi C., Ponnavaikko M., Sundararajan M., (2009),"Improved Kernel Common Vector Method for Face Recognition", Machine Vision, ICMV ''09. Second International Conference on, pp. 13 - 17 11 Serkan G., Edizkan R., (2007),"Use of Novel Feature Extraction Technique with Subspace Classifiers for Speech Recognition", Pervasive Services, IEEE International Conference on, pp. 80 - 83 12 Watanabe S., Lambert P.F., Kulikowski C.A., Buxton J.L. and Walker R., (1967), "Evaluation and selection of variables in pattern recognition", in Compter and Information Sciences ii. New York: Academic, pp. 91. 13 Watanabe S. and pakvasa N., (1973),"Subspace method in pattern recognition", in Proc. 1st Int. Conf. Pattern Recog., Washington, DC, pp. 25-32. 14 陳漢良、李宗寶, (2012)。「利用共同向量法辨識中文單音及爆破氣音的探討」。碩 士論文,國立中興大學統計學研究所,台中。en_US
dc.identifier.urihttp://hdl.handle.net/11455/18757-
dc.description.abstract本篇論文主要是利用共同向量法探討1391個中文單音之辨識。共同向量法是一個簡單且容易應用的方法,不只是可應用在語音辨識而且也可應用在臉部辨識。共同向量法是一種線性子空間分類器,在每一個類別裡,將所有訓練的特徵投影到一個唯一且共同的特徵上作為這個類別的語音模型。這篇論文中,我們運用梅爾頻率倒頻譜係數當作語音的特徵,將中文單音分割成子音與母音兩部分分別作共同向量法,觀察在不同的參數下,如子音母音的加權比重和建構語音模型所運用到的樣本點數的組合,去比較何種參數組合會有較好的辨識率。此次實驗是由十二位不同語者所錄製的語音資料庫去做辨識,權重設定為(子音,母音)=(0.1,0.9)到(0.9,0.1),取樣本點數為1到7。實驗結果發現,當權重設定為(0.5,0.5),取樣本點數為4時,最佳平均辨識率為82%。因此,共同向量法在中文單音辨識上有不錯的辨識率。zh_TW
dc.description.abstractThis paper is to investigate the 1391 monosyllable in speaker-dependent system. The method of common vector is used for the speech recognition. The method is simple and easy in application not only for speech recognition but also for face pattern recognition. The common vector approach is a linear subspace classifier. In each class, it projects all training features into a unique common feature as the model for the corresponding class. In this paper, We use the Mel-frequency cepstrum coefficient (Mfcc) as the feature in the recognition. The mandarin monosyllable divided into consonants and vowels two part, in which the common vector approach is contructed as model, respectively. The different weights are then given for each consonant and vowel parts as the parameters in speech recognition. In the work, the set of training samples will also be considered as the parameter in recognition. From the exprimental results, we find that when the weight equals to (0.5,0.5) for consonant and vowel parts, and the training samples is 4, the best speech recognition will be up to 82%.en_US
dc.description.tableofcontents目錄 摘要 i Abstract ii 目錄 iii 附圖目錄 v 附表目錄 vi 第一章 緒論 1 1.1研究動機 1 1.2相關研究 1 1.3語音的辨識方法 3 1.4論文架構 4 第二章 語音訊號的前處理和特徵值的求取 5 2.1前言 5 2.2語音信號前處理 6 2.2.1數位取樣 6 2.2.2常態化 7 2.2.3端點偵測 8 2.2.4切割音框 9 2.2.5預強調 10 2.2.6視窗化 10 2.3特徵參數的求取 12 2.3.1離散傅立葉轉換(discrete Fourier transform) 12 2.3.2三角濾波器 13 2.3.3頻率範圍 14 2.3.4對數能量 14 2.3.5離散餘弦轉換 14 第三章 語音模型的建立與辨識方法 16 3.1前言 17 3.2建立語音模型 17 3.2.1共同向量法之原理 17 3.2.2差異子空間之介紹 17 3.2.3共同向量的求取 18 3.3子母音部分共同向量法辨識流程和方法 22 第四章 實驗操作流程與實驗結果 25 4.1操作介面 25 4.2實驗參數設定 25 4.2.1語音的來源 25 4.2.2影響辨識率的可能因素 25 4.3實驗結果 26 第五章 結論和建議 32 參考文獻 34zh_TW
dc.language.isozh_TWen_US
dc.publisher統計學研究所zh_TW
dc.relation.urihttp://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0005-0907201317195100en_US
dc.subject梅爾頻率倒頻譜係數zh_TW
dc.subjectMel-frequency cepstrum coefficienten_US
dc.subject特徵擷取zh_TW
dc.subject共同向量法zh_TW
dc.subjectFeature Extractionen_US
dc.subjectCommon vectoren_US
dc.title利用子母音之部分共同向量法於中文單音之辨識zh_TW
dc.titleUsing Common Vector Approach to Recognize Isolated Mandarin Word Based on Vowel and Consonanten_US
dc.typeThesis and Dissertationzh_TW
item.languageiso639-1zh_TW-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
item.openairetypeThesis and Dissertation-
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