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研究生： 楊鎮光
Zhen-Guang Yang
論文名稱： 快速演算法在大字彙關鍵詞萃取上的應用
指導教授： 莊堯棠
Yau-Tarng Juang
口試委員:
學位類別： 碩士
Master
系所名稱： 資訊電機學院 - 電機工程學系
Department of Electrical Engineering
畢業學年度： 89
語文別： 中文
論文頁數： 43
中文關鍵詞： CMS 、樹枝狀 、關鍵字萃取 、快速演算法 、Cepstrum Weighting
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    • 在傳統whole word based的關鍵詞萃取辨識系統中,辨識效能常因關鍵詞彙的增加而導致辨識率下降及辨識時間增加,所謂的快速演算法,就是藉由關鍵詞字彙結構的相關性,將關鍵詞予以分類並加以結構化,因而能藉由樹枝狀的搜尋架構,大幅的減少辨識時間,而隨著關鍵詞彙的增加,辨識率仍能維持ㄧ定水準而不墬,這就是將快速演算法應用在大字彙關鍵詞萃取的目的.
    在作法上,我們先將關鍵詞分成幾個次部分(subsets),而不同關鍵詞的次部分會包含相同的共同次字彙(common subword),如同樹枝一般,在辨識出前N個最佳的共同的次字彙之後,就能夠減小搜尋範圍,捨去不可能入選的關鍵詞,針對相似度比較高的關鍵詞進行最後的確認.進而達到快速的目的.
    除了演算法本身之外,論文中還針對多項能夠提昇辨識率的方案進行實驗,這些方案包含了將無關詞對語音特徵的機率加上一縮小權值,以使關鍵詞的切音區更加準確.使用動態的權值,讓不同的測試語句都有相對應最佳的縮小權值.另外鑒於測試和訓練語料取得環境的不同(分別為電話及麥克風錄音),我們以CMS加上Cepstrum weighting分別對訓練語料及測試語料進行處理,並重新訓練次音節模型,最後,將處理前後(指有無加上CMS及Cepstrum weighting)的機率值混合考慮,並由實驗找出最佳的混合比例.由實驗結果可以發現,動態權值及機率混合考慮這兩種方法如配合使用,可達最佳辨識率Top1為91.32%.而僅使用單一權值的辨識效果最差,Top1達83.67%.
    為了使關鍵詞萃取系統更加完整,關鍵詞拒絕的能力是有必要被加入的,在實驗結果方面,加入關鍵詞拒絕後的正確率為81.51%.

    第一章序論 1 1.1 研究動機…………………………………………………1 1.2 關鍵詞萃取的基本定義………………………………….1 1.3 快速演算法的概念……………………………………….1 1.4技術回顧………………………………………………….2 1.5 論文大綱………………………………………………….4 第二章語音辨識的基本技術 5 2.1 概論…………………………………………………5 2.2 特徵參數的求取…………………………………………5 2.3 隱藏式馬可夫模型………………………………………7 2.3.1 隱藏式馬可夫模型的描述…………………………8 第三章系統架構 11 3.1 概論……………………………………………….11 3.2 模型參數………………………………………………11 3.3 訓練與辨識的演算法…………………………………12 3.3.1 訓練演算法…………………………………………..12 3.3.2 辨識模組與辨識演算法….…………………………15 第四章快速演算法 16 4.1 概論……………………………………………….16 4.2 快速演算法……………………………………………16 4.3 無關詞模組對特徵值機率的縮小權值.……………..21 4.3.1 靜態的縮小權值…………..…………………………21 4.3.2 動態調整縮小權值………..…………………………21 4.4 兩種對特徵值處理的方法─Cepstrum Mean Subtraction和Cepstrum Weighting…………………….……………22 4.4.1 Cepstrum Mean Subtraction和Cepstrum Weighting…22 4.4.2將Cepstrum+Delta Cepstrum及Cepstrum+Delta Cepstrum+CMS+Cepstrum Weighting的機率值加權計算…………………………………………………23 4.5 關鍵詞的拒絕能力.…………………………………23 4.5.1 關鍵詞拒絕的原理…………………………………..23 4.5.2 訓練反模型(anti-model)的方法……………………..24 4.5.3 訓練臨界值τk的方法………………………………25 4.5.4 關鍵詞拒絕的演算法………………………………..26 4.5.5 錯誤率的計算………………………………………..26 第五章實驗與結果 28 5.1 概論……………………………………………….28 5.2 實驗環境………………………………………………28 5.3 大字彙的關鍵詞萃取實驗………………….…………..28 第六章結論 6.1 結論…………………………………………………...…38 6.2 未來發展………………………………………………...39 參考文獻

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