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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 李琳山 | |
| dc.contributor.author | Yen-Chen Wu | en |
| dc.contributor.author | 吳彥諶 | zh_TW |
| dc.date.accessioned | 2021-06-15T12:54:26Z | - |
| dc.date.available | 2019-07-26 | |
| dc.date.copyright | 2016-07-26 | |
| dc.date.issued | 2016 | |
| dc.date.submitted | 2016-07-18 | |
| dc.identifier.citation | [1] Ciprian Chelba, Timothy J Hazen, and Murat Sarac¸lar, “Retrieval and browsing of spoken content,” Signal Processing Magazine, IEEE, vol. 25, no. 3, pp. 39–49, 2008.
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/50719 | - |
| dc.description.abstract | 本論文之主軸在探討語音數位內容之互動式檢索(Interactive Retrieval of Spoken Content)。近年來多媒體數位內容(Multimedia Content) 如線上課程、影音節目、會議錄音等大幅增加，語音數位內容( Spoken Content ) 之檢索也因而大受重視。本論文之目標放在互動式檢索； 由於語音或多媒體文件很難呈現在螢光幕上故瀏覽耗時，而過差的語音辨識率更可能使檢索結果不如人意，因此藉由系統與使用者互動使系統對使用者想找的資訊有更多瞭解，是一個有效改善此一問題的方法。
在本論文中，我們不但模仿前人用馬可夫決策模型(Markov Decision Process，MDP)來建立互動式檢索的模型，採用強化學習(Reinforcement Learning)演算法來學習出最佳系統決策， 更使用深層強化學習(Deep Reinforcement Learning)解決問題讓整個技術向前邁進一大步。實驗顯示，我們提出的方法確實能夠大幅改善檢索程序，幫助使用者更有效的找到所要找的資訊。 | zh_TW |
| dc.description.abstract | Interactive retrieval is important for spoken content. The reason is because when looking for text documents, one can easily scan through and select on a search engine result page, whereas similar privileges don not exist when searching for spoken content. Besides, it is hard for the users to find the desired spoken content when the search results are noisy, which usually happens due to the imperfect speech recognition components in spoken content retrieval. A way to counter the difficulties of spoken content retrieval is human-machine interaction that machine takes different actions to request additional information from the user to obtain better retrieval results. The most suitable actions depend on the situations, so in previous works, some hand-crafted states estimated from the current search results are used to determine the actions, but the hand-crafted states are not necessary the best indicator for choosing actions. In this paper, we applied the Deep-Q- Learning method in interactive retrieval of spoken content. Deep-Q- Learning sidesteps the estimation of the hand-crafted states and can directly determine the action based on retrieval results without any human knowledge. It reached discernible improvements compared with the hand-crafted states. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-15T12:54:26Z (GMT). No. of bitstreams: 1 ntu-105-R03942044-1.pdf: 4686277 bytes, checksum: 0381461eeeebcf5aefbea07b8cea2b88 (MD5) Previous issue date: 2016 | en |
| dc.description.tableofcontents | 誌謝. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i
中文摘要. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii 英文摘要. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii 一、導論. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1 背景介紹. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 研究動機與目的. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.3 相關研究與主要貢獻. . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.4 章節安排. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 二、基礎背景知識. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.1 自動語音辨識系統. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.1.1 聲學模型. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.1.2 語言模型. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.1.3 詞典. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.1.4 詞圖與N 最佳序列. . . . . . . . . . . . . . . . . . . . . . . . 8 2.1.5 辭典外詞彙(Out of Vocabulary， OOV) . . . . . . . . . . . . . 10 2.2 資料搜尋系統. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.2.1 語音文件搜尋. . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.2.2 語音文件索引. . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.2.3 向量空間檢索模型(VSM) . . . . . . . . . . . . . . . . . . . . 14 2.2.4 語言模型檢索模型(LM) . . . . . . . . . . . . . . . . . . . . . 15 2.2.5 資訊檢索評估機制. . . . . . . . . . . . . . . . . . . . . . . . 16 2.3 互動式系統. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.3.1 相關回饋. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.3.2 對話管理者- 馬可夫決策模型(MDP) . . . . . . . . . . . . . . 20 2.3.3 人工類神經網路. . . . . . . . . . . . . . . . . . . . . . . . . . 23 2.4 本章總結. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 三、互動式語音資訊檢索系統. . . . . . . . . . . . . . . . . . . . . . . . . . . 26 3.1 簡介. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 3.1.1 前人研究與改進動機. . . . . . . . . . . . . . . . . . . . . . . 26 3.1.2 系統組成. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 3.2 語音檢索的相關回饋. . . . . . . . . . . . . . . . . . . . . . . . . . . 28 3.2.1 向量空間模型相關回饋. . . . . . . . . . . . . . . . . . . . . . 29 3.2.2 語言模型相關性回饋. . . . . . . . . . . . . . . . . . . . . . . 30 3.3 本章總結. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 四、深層強化學習與對話管理者. . . . . . . . . . . . . . . . . . . . . . . . . . 34 4.1 簡介. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 4.2 馬可夫決策模型模擬互動式搜尋系統. . . . . . . . . . . . . . . . . . 34 4.2.1 狀態(State) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 4.2.2 補充：狀態特徵值(state feature) . . . . . . . . . . . . . . . . . 37 4.2.3 行動(Action) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 4.2.4 獎勵(Reward)與總回報(Return) . . . . . . . . . . . . . . . . . 41 4.3 強化學習演算法. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 4.3.1 值迭代學習法. . . . . . . . . . . . . . . . . . . . . . . . . . . 43 4.3.2 貼合值迭代學習法. . . . . . . . . . . . . . . . . . . . . . . . 44 4.3.3 深層Q-類神經網路(Deep Q-Network, DQN) . . . . . . . . . . 46 4.4 系統評估. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 4.4.1 實驗設定. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 4.4.2 模擬使用者. . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 4.4.3 實驗結果. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 4.5 互動式搜尋系統結論. . . . . . . . . . . . . . . . . . . . . . . . . . . 55 五、模擬使用者. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 5.1 簡介. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 5.2 使用者調查. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 5.2.1 問卷設計與其內容. . . . . . . . . . . . . . . . . . . . . . . . 59 5.2.2 調查結果. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 5.3 實驗分析. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 5.3.1 互動行為代價實驗. . . . . . . . . . . . . . . . . . . . . . . . 65 5.3.2 使用者行為實驗. . . . . . . . . . . . . . . . . . . . . . . . . . 67 5.4 結論. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 六、結論與未來展望. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 6.1 對話管理者. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 6.2 模擬使用者. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 6.3 系統架構. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 參考文獻. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 | |
| dc.language.iso | zh-TW | |
| dc.subject | 互動式資訊檢索 | zh_TW |
| dc.subject | 深層強化學習 | zh_TW |
| dc.subject | 語音數位內容檢索 | zh_TW |
| dc.subject | 互動式資訊檢索 | zh_TW |
| dc.subject | 深層強化學習 | zh_TW |
| dc.subject | 語音數位內容檢索 | zh_TW |
| dc.subject | Interactive Information Retrieval | en |
| dc.subject | Spoken Content Retrieval | en |
| dc.subject | Deep Reinforcement Learning | en |
| dc.subject | Spoken Content Retrieval | en |
| dc.subject | Interactive Information Retrieval | en |
| dc.subject | Deep Reinforcement Learning | en |
| dc.title | 使用深層強化學習之互動式語音數位內容檢索 | zh_TW |
| dc.title | Interactive Spoken Content Retrieval with Deep Reinforcement Learning | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 104-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 李宏毅,陳信宏,王小川,鄭秋豫,簡仁宗 | |
| dc.subject.keyword | 深層強化學習,互動式資訊檢索,語音數位內容檢索, | zh_TW |
| dc.subject.keyword | Deep Reinforcement Learning,Interactive Information Retrieval,Spoken Content Retrieval, | en |
| dc.relation.page | 80 | |
| dc.identifier.doi | 10.6342/NTU201600891 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2016-07-18 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電信工程學研究所 | zh_TW |
| 顯示於系所單位： | 電信工程學研究所 | |
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