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研究生:羅永典
研究生(外文):Yueng-Tien Lo
論文名稱:使用邊際資訊於鑑別式聲學模型訓練
論文名稱(外文):A Study on Margin-Based Discriminative Training of Acoustic Models
指導教授:陳柏琳陳柏琳引用關係
指導教授(外文):Chen, Berlin
學位類別:碩士
校院名稱:國立臺灣師範大學
系所名稱:資訊工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2010
畢業學年度:98
語文別:中文
論文頁數:51
中文關鍵詞:語音辨識聲學模型鑑別式訓練邊際資訊資料選取
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本論文旨在探究近年具代表性的鑑別式聲學模型訓練方法及其背後之一致性,並且延伸發展各種不同以邊際為基礎的資料選取方法來改善鑑別式聲學模型訓練,應用於中文大詞彙連續語音辨識。首先,為了進一步探討近年各種鑑別式訓練方法,我們整理歸納近年所發展鑑別式訓練方法之目標函數其背後一致性。其次,我們討論了各種不同邊際資訊應用於鑑別式訓練的方法,進而在大詞彙連續語音辨識中有效地降低語音辨識錯誤率。再者,我們結合了柔性邊際與增進式方法使得在資料選取的範圍上更為明確且具彈性,以提供更具鑑別資訊的統計量。在實作上,我們觀察了以語句為層次的選取資料為例,以進一步了解各式統計資訊對於鑑別式訓練成效之影響。最後,本論文以公視新聞語料做為實驗平台,實驗結果初步證實了本論文所提出之作法在某種程度上能夠改善過去方法所面臨的過度訓練之問題。
This thesis sets the goal at investigating the consistency properties underlying the most popular algorithms for discriminative training of acoustic models. Various margin- and boosting-based training data selection methods are also extensively explored in conjunction with the discriminative training algorithms for Mandarin large vocabulary continuous speech recognition (LVCSR). First, for providing an in-depth evaluation of the utilities of the discriminative acoustic model training algorithms developed recently, we try to deduce the consistency properties from their individual training objectives. Second, we compare among different margin- and boosting-based methods that have the abilities to make acoustic training concentrate more on discriminative training data so as to effectively enhance the LVCSR performance. Furthermore, we also attempt to pair the soft-margin- with the boosting-based methods to make good use of more discriminative statistics, while the implementation is instantiated by utterance-level data selection. All experiments are conducted on a Mandarin broadcast news corpus compiled in Taiwan, and the associated results seem to demonstrate that the proposed approaches could relieve the over-training problem to a certain extent.
第一章 序論 1
1.1 研究背景與動機 1
1.2 統計式語音辨識架構 1
1.2.1. 特徵擷取(前端處理) 3
1.2.2. 聲學模型 4
1.2.3. 語言模型 5
1.2.4. 語言解碼 6
1.3 本論文研究內容與貢獻 7
1.4 論文架構 7
第二章 鑑別式訓練法則及其一致性 9
2.1 貝氏風險與全面風險 9
2.2 最小化分類錯誤法則(Minimum Classification Error) 11
2.3 最大交互資訊法則 (Maximum Mutual Information) 13
2.4 最小化音素錯誤訓練 (Minimum Phone Error) 14
2.5 鑑別式訓練方法之一致性 15
第三章 邊際資訊與資料選取方法於鑑別式訓練 17
3.1 最大邊際估測法則 (Large-Margin Estimation) 17
3.2 柔性邊際估測法則 (Soft Margin Estimation) 20
3.3 以強化混淆資訊為出發的邊際因子-增進式最大交互資訊法則(Boosted MMI) 23
3.4 以邊際資訊與錯誤為基礎的統一觀點 25
3.5 從邊際資訊方法到增進式權重因子 27
第四章 實驗環境架構與基礎實驗 30
4.1 臺師大中文大詞彙連續語音辨識系統 30
4.1.1. 前端處理 30
4.1.2. 聲學模型 30
4.1.3. 辭典建立與語言模型訓練 31
4.1.4. 詞彙樹複製搜尋 32
4.2 實驗語料與評估方式 33
4.2.1. 實驗語料 33
4.2.2. 實驗評估方式 33
4.3 基礎實驗結果 34
第五章 實驗結果與討論 35
5.1. 邊際概念方法實驗結果 35
5.1.1. 語句層級柔性邊際估測法則實驗及觀察 35
5.1.2. 增進式最大化交互資訊訓練法則(BMMI) 40
5.1.3. 結合柔性邊際估測法與增進式因子(Hybrid-MMI) 42
第六章 結論與未來展望 44
參考文獻 45
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