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研究生:溫宗憲
研究生(外文):Tsung-Hsien Wen
論文名稱:具備語音功能的雲端應用: 個人化語言模型與互動式語音文件檢索
論文名稱(外文):Voice Access of Cloud Applications : Language Model Personalization and Interactive Spoken Content Retrieval
指導教授:李琳山李琳山引用關係
指導教授(外文):Lin-shan Lee
口試委員:鄭秋豫王小川陳信宏簡仁宗
口試委員(外文):Chiu-yu TsengHsiao-Chuan WangSin-Horng ChenJen-Tzung Chien
口試日期:2013-06-24
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:電機工程學研究所
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:2013
畢業學年度:101
語文別:中文
論文頁數:98
中文關鍵詞:個人化語言模型社群網路互動式檢索馬可夫決策模型強化學習
外文關鍵詞:Personalized Language ModelingSocial NetworkInteractive RetrievalMarkov Decision ProcessReinforcement Learning
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本論文探討具備語音功能的兩項雲端應用技術:個人化的語言模型
及互動式語音文件檢索。
在語音辨識中,模型的不匹配對辨識率一向有很大的損害。由於個
人化手機普及,個人化辨識系統成為可行,而考量到每個個人語言使
用習慣的差異,語言模型的個人化有其必要。過去個人語料庫建立不
易,但如今,越來越多人習慣性地在社群網站上留下大量的文章與留
言,故個人化語料庫較以前容易取得許多。但資料稀疏的問題仍然不
易解決。在本論文中,我們提出以各種方式估計社群網站上不同使用
者間的用語相似度,並據以加入不同使用者的語料庫來幫助估計更強
健的個人化語言模型。我們並比較了用N 連文法語言模型及遞迴式類
神經網路語言模型來實做的效能表現,並驗證了新提出的方法確實提
升了對個人語言的預測能力。
在第二部分裡,我們探討互動式語音文件檢索。由於語音文件很難
呈現且瀏覽耗時,而過差的辨識率更可能使檢索結果不如人意,因此
藉由與使用者互動使系統對使用者想找的資訊有更多瞭解,是一個有
效改善此問題的方法。在本論文中,我們用馬可夫決策模型(Markov
Decision Process, MDP) 來模擬互動式檢索的問題,並採用強化學習
(Reinforcement Learning) 演算法學習出最佳系統決策,亦採用不同的檢
索模型來實作檢索系統。實驗顯示,我們提出的方法確實能夠輔助檢
索進行,幫助使用者更有效的找到所要找的資訊。

This thesis considers voice access of cloud applications with two parts: (1)
Personalized Language Model and (2) Interactive spoken document retrieval.
Model mismatch has been a major problem in speech recognition. With
hand-held devices widely used today, personalized models become possible.
A huge quantities of posts and comments with known owners emerged on
social network websites, personal corpora become practically available but
with data sparseness problem unsolved. In the first part of this thesis, we
proposed personalized language modeling approaches by estimating the language
similarities between different social network users and integrating the
corresponding personal corpora accordingly. We studied both N-gram language
models as well as recurrent neural network language models, and the
experimental results support the concept.
In the second part of this thesis, we studied interactive spoken document
retrieval. Interactive retrieval is helpful to spoken content retrieval because
retrieved spoken items are difficult to be shown on screen and browsed by
the user, in addition to the speech recognition uncertainty. We model the
interaction process by a Markov Decision Process and train the policy with
Reinforcement Learning. Experimental results demonstrate the retrieval performance
can be improved with the interactions.

口試委員會審定書 i
致謝 ii
中文摘要 iv
Abstract v
Contents vi
List of Figures x
List of Tables xii
1 導論 1
1.1 具備語音界面的雲端應用平台. . . . . . . . . . . . . . . . . . . . . . 1
1.1.1 個人化語音辨識系統. . . . . . . . . . . . . . . . . . . . . . . 3
1.1.2 互動式搜尋系統. . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.2 章節安排. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2 基礎背景簡介 7
2.1 個人化語音辨識系統. . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.1.1 聲學模型與調適. . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.1.2 N 連文法語言模型(N-gram) . . . . . . . . . . . . . . . . . . . 9
2.1.3 類神經網路語言模型(NNLM) . . . . . . . . . . . . . . . . . . 10
2.1.4 語言模型調適. . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.2 互動式搜尋系統. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.2.1 語音文件搜尋. . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.2.2 語音文件索引. . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.2.3 向量空間檢索模型(VSM) . . . . . . . . . . . . . . . . . . . . 18
2.2.4 語言模型檢索模型(LM) . . . . . . . . . . . . . . . . . . . . . 19
2.2.5 對話管理者- 馬可夫決策模型(MDP) . . . . . . . . . . . . . . 20
I 個人化語音辨識系統 23
3 社群網路群力模式 24
3.1 群力模式概念介紹. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
3.2 具備語音界面的社群網路瀏覽器. . . . . . . . . . . . . . . . . . . . . 26
3.2.1 系統特點. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
3.2.2 臉書認證機制. . . . . . . . . . . . . . . . . . . . . . . . . . . 27
3.2.3 系統生態系. . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
4 個人化語言模型 29
4.1 N 連文法語言模型(N-gram LM) 個人化. . . . . . . . . . . . . . . . . 29
4.1.1 模型調適整體架構. . . . . . . . . . . . . . . . . . . . . . . . 31
4.1.2 基於社群網路互動關係之相似度估計. . . . . . . . . . . . . . 32
4.1.3 基於潛藏式主題模型之相似度估計. . . . . . . . . . . . . . . 33
4.1.4 基於隨機漫步演算法之相似度重估. . . . . . . . . . . . . . . 35
4.1.5 語句集群法. . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
4.2 個人化N 連文法語言模型評估. . . . . . . . . . . . . . . . . . . . . . 37
4.2.1 實驗設定. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
4.2.2 混淆度實驗結果與結果分析. . . . . . . . . . . . . . . . . . . 39
4.2.3 語音辨識實驗結果. . . . . . . . . . . . . . . . . . . . . . . . 41
4.3 遞迴式類神經網路語言模型(RNNLM) 的個人化. . . . . . . . . . . . 43
4.3.1 模型結構. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
4.3.2 最佳化演算法- 沿時間反向傳播(BPTT) . . . . . . . . . . . . 45
4.3.3 上下文相關的遞迴式類神經網路與詞輔助特徵. . . . . . . . 46
4.3.4 三步驟調適機制. . . . . . . . . . . . . . . . . . . . . . . . . . 47
4.3.5 使用者導向的詞特徵. . . . . . . . . . . . . . . . . . . . . . . 50
4.4 個人化遞迴式類神經網路語言模型評估. . . . . . . . . . . . . . . . . 52
4.4.1 實驗設定. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
4.4.2 混淆度實驗結果與結果分析. . . . . . . . . . . . . . . . . . . 53
4.4.3 前N 最佳結果重評分實驗結果. . . . . . . . . . . . . . . . . . 55
4.5 個人化語言模型結論. . . . . . . . . . . . . . . . . . . . . . . . . . . 58
5 個人化語言模型與個人化聲學模型整合 59
5.1 個人化聲學模型. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
5.2 整合系統架構. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
II 互動式搜尋系統 62
6 互動式語音搜尋系統 63
6.1 簡介. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
6.1.1 研究動機. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
6.1.2 系統組成. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
6.2 語音檢索的相關回饋. . . . . . . . . . . . . . . . . . . . . . . . . . . 65
6.2.1 向量空間模型相關回饋. . . . . . . . . . . . . . . . . . . . . . 66
6.2.2 語言模型相關性回饋. . . . . . . . . . . . . . . . . . . . . . . 67
7 馬可夫決策模型作為對話管理員 70
7.1 馬可夫決策模型模擬互動式搜尋系統. . . . . . . . . . . . . . . . . . 70
7.1.1 狀態(State) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
7.1.2 行動(Action) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
7.1.3 獎勵(Reward) 與回報(Return) . . . . . . . . . . . . . . . . . . 73
7.2 強化學習演算法. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
7.2.1 值迭代學習法. . . . . . . . . . . . . . . . . . . . . . . . . . . 74
7.2.2 貼合值迭代學習法. . . . . . . . . . . . . . . . . . . . . . . . 74
7.3 狀態估計. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
7.3.1 狀態特徵值. . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
7.3.2 離散狀態估計- 多類別支持向量機. . . . . . . . . . . . . . . 78
7.3.3 連續狀態估計- 正規化線性回歸法. . . . . . . . . . . . . . . 80
7.4 系統評估. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
7.4.1 實驗設定. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
7.4.2 模擬使用者. . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
7.4.3 離散空間向量模型初始實驗. . . . . . . . . . . . . . . . . . . 82
7.4.4 離散、連續空間語言模型實驗與結果比較. . . . . . . . . . . 84
7.5 互動式搜尋系統結論. . . . . . . . . . . . . . . . . . . . . . . . . . . 86
8 結論與未來展望 87
8.1 雲端化的應用程式平台. . . . . . . . . . . . . . . . . . . . . . . . . . 87
8.2 個人化語音辨識系統. . . . . . . . . . . . . . . . . . . . . . . . . . . 87
8.3 互動式搜尋系統. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
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