Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/18153
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dc.contributor邱國欽zh_TW
dc.contributor郭仁泰zh_TW
dc.contributor.advisor李宗寶zh_TW
dc.contributor.author籃元隆zh_TW
dc.contributor.authorLan, Yuan-Lungen_US
dc.contributor.other中興大學zh_TW
dc.date2010zh_TW
dc.date.accessioned2014-06-06T07:02:58Z-
dc.date.available2014-06-06T07:02:58Z-
dc.identifierU0005-2906200918245900zh_TW
dc.identifier.citation[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”。台北市:旗標。 [3]吳明哲,黃世陽(1998)。 “Visual Basic 6.0 中文版學習範本”。台北市:松崗。 [4]林子傑,李宗寶(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。 [5]張國清,李宗寶(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。 [6]楊茗惠,黎自奮,李宗寶(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研究所,台中。 [7]Atal, B.S. and Hanauer, S.L. (1971). “Speech analysis and synthesis by linear prediction of the speech wave”, J. Acoust. Soc. Am. vol. 50, pp. 637-655. [8]Bing, X. and Yihe, S. (1996). “Research on ASIC for multi-speaker isolated word recognition”, ASIC, 2nd International Conference, pp. 135-137. [9]Cover, T.M. and Hart, P.E. (1967). “Nearest Neighbor Pattern Classification”, IEEE Trans. On Information Theory, vol. IT-13, No. 1, pp. 21-27. [10]Cevikalp, H. and Neamtu, M. (2005). “Discriminative Common Vectors for Face Recognition”, IEEE Trans. On Pattern Analysis and Machine Intelligence, vol. 27, No. 1, pp. 4-13. [11]Chu, M.K. and Sohn, Y.S. (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, pp. 620-627. [13]Higgins, A.L., Bahler, L.G.. and Porter, J.E. (1993). “Voice identification using nearest-neighbor distance measure”, ICASSP, vol. 2, pp. 375-378. [14]Tran, T.N., Wehrens, R. and Buydens, L.M.C. (2006). “KNN-kernel density-based clustering for high-dimensional multivariate data”, Computational Statistics and Data Analysis, vol. 51, pp. 513-525.zh_TW
dc.identifier.urihttp://hdl.handle.net/11455/18153-
dc.description.abstract本篇論文主要是探討200個國語單字的特定語者單音辨識,所使用的辨識方法為權重式多重K-最近鄰居(KNN)法。我們考慮影響辨識率的實驗因子有「音框數」、「權重比例」及「K值的選用」等三種,想試著找出在何種組合之下會達到最佳的辨識率。而本實驗結果,在辨識200個國語單字時,最高辨識率達到90.50%。zh_TW
dc.description.abstractThis paper is mainly to discuss the speech recognition of 200 isolated mandarin words for Speaker-Dependent System and we use the method of weighted multiple K-Nearest Neighbor. We consider the three experimental factors such as “the number of frame”, “the proportion of weight” and “the number of K” for the rate of recognition and try to find out the best rate under which kind of combination. By the experimental result, we recognize 200 isolated mandarin words which attain 90.50% for the best rate of recognition.en_US
dc.description.tableofcontents中文摘要..............................................i Abstract.............................................ii 目錄................................................iii 附圖目錄.............................................vi 附表目錄............................................vii 第一章 緒論...........................................1 1.1研究動機與目的.....................................1 1.2語音專有名詞簡介...................................1 1.2.1子音和母音...................................1 1.2.2音強(intensity) .............................2 1.2.3音頻(voice frequency) .......................2 1.2.4音色(quality) ...............................2 1.2.5遮蔽效應(masking effect) ....................3 1.3語音辨識介紹.......................................3 1.3.1語音的不穩定性...............................3 1.3.2語音的研究範圍...............................4 1.3.3幾種建立語音模型的方法.......................5 1.4語音辨識流程概述...................................5 1.4.1語音前處理與特徵參數的求取...................6 1.4.2音框數的壓縮與擴張...........................8 1.4.3前置作業.....................................9 1.4.4辨識比對....................................10 1.5論文架構..........................................11 第二章 語音訊號的前處理與特徵參數的求取..............13 2.1前言..............................................13 2.2語音訊號前處理....................................13 2.2.1數位取樣....................................13 2.2.2常態化......................................14 2.2.3端點偵測....................................15 2.2.4切割音框....................................17 2.2.5預強調......................................17 2.2.6視窗化......................................18 2.3特徵參數的求取....................................19 2.3.1自相關函數(autocorrelation function)........20 2.3.2線性預估編碼(linear predict coding;LPC) ...20 2.3.3倒頻譜參數(cepstrum coefficient;CPT) ......22 第三章 語音前置作業與辨識方法........................23 3.1前言..............................................23 3.2音框數的壓縮與擴張................................23 3.3語音前置作業......................................25 3.3.1 K-最近鄰居法(K-nearest neighbor;KNN) .....25 3.3.2語音分母音群................................26 3.3.3尋找最佳化音框與權重........................27 3.4辨識方法..........................................29 3.4.1待測語音的處理..............................29 3.4.2比對的方法..................................29 第四章 實驗操作流程與實驗結果........................34 4.1操作介面..........................................34 4.2實驗流程..........................................34 4.2.1語音的來源..................................34 4.2.2影響辨識率的可能因子........................35 4.2.3辨識結果....................................35 4.2.4應用於其他字彙量的辨識結果..................39 第五章 結論與建議....................................41 參考文獻.............................................42 附錄.................................................44zh_TW
dc.language.isoen_USzh_TW
dc.publisher應用數學系所zh_TW
dc.relation.urihttp://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0005-2906200918245900en_US
dc.subjectK-Nearest Neighboren_US
dc.subjectK-最近鄰居法zh_TW
dc.title利用權重式多重KNN法於中字彙之特定語者中文單音辨識zh_TW
dc.titleUsing the Method of Weighted multiple-KNN to Recognize Isolated Mandarin Word for Speaker-Dependent Systemen_US
dc.typeThesis and Dissertationzh_TW
item.fulltextno fulltext-
item.languageiso639-1en_US-
item.openairetypeThesis and Dissertation-
item.cerifentitytypePublications-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextnone-
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