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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 李琳山 | |
| dc.contributor.author | Chia-Ping Chen | en |
| dc.contributor.author | 陳佳蘋 | zh_TW |
| dc.date.accessioned | 2021-06-15T06:43:24Z | - |
| dc.date.available | 2011-07-25 | |
| dc.date.copyright | 2011-07-25 | |
| dc.date.issued | 2011 | |
| dc.date.submitted | 2011-07-06 | |
| dc.identifier.citation | [1] http://www2.sims.berkeley.edu/research/projects/how-much-info/internet.html.
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/47950 | - |
| dc.description.abstract | 一般而言，語音資訊檢索會先透過語音辨識，將語料庫中的語音轉換為文字，再對辨識出的文字進行檢索。然而，這樣的系統架構非常仰賴好的語音辨識系統。如果語音辨識的辨識率很低，檢索系統無法根據辨識出的文字來判斷查詢問句所在的語句，則語音資訊檢索的效能會大幅下降。
本論文提出兩種以聲學特徵相似度來改進語音資訊檢索的方法：虛擬相關回饋及圖學基礎之重排序； 其優點在於以非監督(Unsupervised) 的方法，透過聲學特徵的比對，有效彌補因辨識率低造成的檢索效能下降。在虛擬相關回饋部分，我們定義聲學特徵相似分數，並提出三種虛擬相關語句選擇的方法。在圖學基礎之重排序部分，我們以聲學特徵相似度建立語句關係圖，並套用隨機漫步及修 正隨機漫步演算法來重新分配相關分數。我們並結合兩種方法，達到最好的語音檢索效能。在辨識率為62:55% 的辨識系統下，語音資訊檢索的平均準確率從55:54%進步至70:61%，相對進步率為27%。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2021-06-15T06:43:24Z (GMT). No. of bitstreams: 1 ntu-100-R98942076-1.pdf: 5690299 bytes, checksum: 3c9120d4db48ada8070dc8c140ea0873 (MD5) Previous issue date: 2011 | en |
| dc.description.tableofcontents | 口試委員會審定書. . . . . . . . . . . . . . . . . . . . i
誌謝. . . . . . . . . . . . . . . . . . . . . . . . . . ii 中文摘要. . . . . . . . . . . . . . . . . . . . . . . . iii 一、緒論. . . . . . . . . . . . . . . . . . . . . . . . 1 1.1 研究背景. . . . . . . . . . . . . . . . . . . . . . 1 1.2 研究動機. . . . . . . . . . . . . . . . . . . . . . 1 1.3 本論文研究貢獻. . . . . . . . . . . . . . . . . . . 2 1.4 章節安排. . . . . . . . . . . . . . . . . . . . . . 3 二、背景知識. . . . . . . . . . . . . . . . . . . . . . 4 2.1 資訊檢索. . . . . . . . . . . . . . . . . . . . . . 4 2.2 語音資訊檢索. . . . . . . . . . . . . . . . . . . . 6 2.2.1 語音資訊檢索背景. . . . . . . . . . . . . . . . . 7 2.2.2 相關回饋. . . . . . . . . . . . . . . . . . . . . 10 2.2.3 重排序. . . . . . . . . . . . . . . . . . . . . . 12 2.3 資訊檢索評估機制. . . . . . . . . . . . . . . . . . 14 三、傳統語音資訊檢索. . . . . . . . . . . . . . .. . . .16 3.1 抽取聲學特徵. . . . . . . . . . . . . . . . . . . . 16 3.2 語音辨識. . . . . . . . . . . . . . . . . . . . . . 19 3.2.1 聲學模型. . . . . . . . . . . . . . . . . . . . . 20 3.2.2 語言模型. . . . . . . . . . . . . . . . . . . . . 22 3.2.3 辨識結果. . . . . . . . . . . . . . . . . . . . . 23 3.3 檢索系統. . . . . . . . . . . . . . . . . . . . . . 23 四、實驗語料及背景介紹. . . . . . . . . . . . . . . . . 26 4.1 檢索測試語料. . . . . . . . . . . . . . . . . . . . 26 4.2 聲學特徵. . . . . . . . . . . . . . . . . . . . . . 26 4.3 語音辨識. . . . . . . . . . . . . . . . . . . . . . 27 4.3.1 聲學模型. . . . . . . . . . . . . . . . . . . . . 27 4.3.2 辭典與語言模型. . . . . . . . . . . . . . . . . . 29 4.4 查詢問句. . . . . . . . . . . . . . . . . . . . . . 30 4.5 基準實驗(Baseline). . . . . . . . . . . . . . . . . 30 五、虛擬相關回饋. . . . . . . . . . . . . . . . . . . . 32 5.1 系統架構. . . . . . . . . . . . . . . . . . . . . . 32 5.2 聲學距離. . . . . . . . . . . . . . . . . . . . . . 34 5.2.1 相符區域(Hit Region). . . . . . . . . . . . . . . 35 5.2.2 動態時軸校正(Dynamic Time Warping). . . . . . . . 36 5.3 聲學特徵相似分數. . . . . . . . . . . . . . . . . . 38 5.4 與原始相關分數整合. . . . . . . . . . . . . . . . . 40 5.5 虛擬相關語句選擇. . . . . . . . . . . . . . . . . . 41 5.5.1 依原始相關分數選擇. . . . . . . . . . . . . . . . 41 5.5.2 依聲學距離向量選擇. . . . . . . . . . . . . . . . 41 5.5.3 依最小距離選擇. . . . . . . . . . . . . . . . . . 43 5.6 實驗與分析. . . . . . . . . . . . . . . . . . . . . 43 5.6.1 虛擬相關回饋. . . . . . . . . . . . . . . . . . . 43 5.6.2 參數影響. . . . . . . . . . . . . . . . . . . . . 45 六、圖學基礎之重排序. . . . . . . . . . . . . . . . . . 50 6.1 系統架構. . . . . . . . . . . . . . . . . . . . . . 50 6.2 聲學分佈關係圖. . . . . . . . . . . . . . . . . . . 52 6.3 圖學基礎演算法. . . . . . . . . . . . . . . . . . . 53 6.3.1 隨機漫步演算法(Random Walk) . . . . . . . . . . . 53 6.3.2 修正隨機漫步演算法. . . . . . . . . . . . . . . . 54 6.4 與原始相關分數整合. . . . . . . . . . . . . . . . . 55 6.5 實驗與分析. . . . . . . . . . . . . . . . . . . . . 55 6.5.1 圖學基礎之重排序. . . . . . . . . . . . . . . . . 56 6.5.2 參數影響. . . . . . . . . . . . . . . . . . . . . 58 6.6 與虛擬相關回饋整合. . . . . . . . . . . . . . . . . 62 七、結論與展望. . . . . . . . . . . . . . . . . . . . . 64 7.1 總結. . . . . . . . . . . . . . . . . . . . . . . . 64 7.2 未來展望. . . . . . . . . . . . . . . . . . . . . . 65 7.2.1 考慮語者變異. . . . . . . . . . . . . . . . . . . 65 7.2.2 考慮音框比重. . . . . . . . . . . . . . . . . . . 66 7.2.3 考慮語言資訊. . . . . . . . . . . . . . . . . . . 66 參考文獻. . . . . . . . . . . . . . . . . . . . . . . . 67 | |
| dc.language.iso | zh-TW | |
| dc.subject | 語音資訊檢索 | zh_TW |
| dc.subject | 虛擬相關回饋 | zh_TW |
| dc.subject | 口語詞彙偵測 | zh_TW |
| dc.subject | Pseudo-Relevance Feedback | en |
| dc.subject | Speech Information Retireval | en |
| dc.subject | Spoken Term Detection | en |
| dc.title | 以聲學特徵相似度改進語音資訊檢索 | zh_TW |
| dc.title | Improved Speech Information Retrieval by Acoustic Feature Similarity | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 99-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 陳信宏,鄭秋豫,王小川,簡仁宗 | |
| dc.subject.keyword | 語音資訊檢索,口語詞彙偵測,虛擬相關回饋, | zh_TW |
| dc.subject.keyword | Speech Information Retireval,Spoken Term Detection,Pseudo-Relevance Feedback, | en |
| dc.relation.page | 79 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2011-07-06 | |
| 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 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-100-1.pdf 未授權公開取用 | 5.56 MB | Adobe PDF |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。