博碩士論文 102521073 詳細資訊




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姓名 蘇仲潔(Zhong-Jie Su)  查詢紙本館藏   畢業系所 電機工程學系
論文名稱 利用語者特定背景模型之語者確認系統
(Speaker Verification using Speaker Dependent Background Models)
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摘要(中) 在一般的語者確認系統中,有兩種背景語者模型的選取方法,分別為通用背景模型(Universal Background Model,UBM)與反語者模型(Anti-SpeakerModel),其兩種方法都存在著各自的缺點,將會使系統的效能下降。所以在本論文中我們將對背景語者模型進行研究與改良,主要以改善以上兩種背景語者模型之缺點、提升辨識效能為目的,並提出背景語者模型之新準則,以及找出一個符合此準則的目標函式,然後將每個由通用背景模型所調適出來的語者特定模型,分別建立其專屬的背景語者模型,在本論文中我們稱這些符合新準則的模型為語者特定背景模型(Speaker Dependent Background Model, SDBM)。語者特定背景模型將可改善傳統反語者模型與通用背景模型的部分缺點,並增進語者確認的效果,其效果將以實驗來予以驗證。
摘要(英) Universal background model (UBM) and anti-speaker model are two methods of background models for a speaker verification system in general. But they existed a few problems.
Therefore we propose two criteria for determining background models. The created new background model is called the speaker dependent background model (SDBM). The results of experiments show that the SDBM improves the performance of the UBM and anti-speaker model approaches.
關鍵字(中) ★ 語者確認
★ 通用背景模型
★ 反語者模型
關鍵字(英) ★ Speaker Verification
★ Universal Background Model
★ Anti-Model
論文目次 摘要 I
Abstract II
圖目錄 V
表目錄 VI
第一章 緒論 1
1.1 研究動機與背景 1
1.2 語者辨識架構概述 4
1.3 語者調適概述 6
1.4 文獻探討 7
1.4.1 最小錯誤鑑別式(Minimum Classification Error, MCE) 7
1.4.2 聯合因子分析(Joint Factor Analysis, JFA) 10
1.4.3 UB-DNorm 15
1.4.4 測試正規化(Test Normalization, TNorm) 16
1.4.5 DPSO調適法 17
1.4.6 RWRS確認系統 19
1.5 研究方向 21
1.6 章節概要 23
第二章 語者辨識基礎之技術 24
2.1 預處理 25
2.1.1 取音框 26
2.1.2 預強調 27
2.1.3 特徵參數擷取 27
2.2 語者模型之訓練 28
2.2.1 高斯混合模型 29
2.2.2 向量量化 31
2.2.3 EM演算法 34
2.3 語者模型之調適 36
2.4 語者確認端 39
第三章 背景語者模型 41
3.1 反語者模型 41
3.1.1 反語者模型選擇方法 43
3.2 通用背景模型 43
第四章 語者特定背景模型 45
4.1 SDBM準則 46
4.2 SDBM調適法 47
4.2.1 競爭語者選取法 50
4.2.2 SDBM目標函式 50
4.2.3 綜合機率減少演算法 52
4.3 SDBM語者確認系統 54
第五章 實驗結果與討論 55
5.1 語音資料庫 55
5.2 效能評估 56
5.2.1 相等錯誤率 56
5.2.2 決策成本函數 57
5.3 實驗結果 58
5.3.1 實驗一 四種調適法之效能比較 58
5.3.2 實驗二 四種調適法之速度比較 61
5.3.3 實驗三 五種確認系統之效能比較 62
5.3.4 實驗四 五種確認系統之速度比較 64
第六章 結論與未來展望 66
6.1 結論 66
6.2 未來展望 67
參考文獻 68
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指導教授 莊堯棠(Yau-tarng Juang) 審核日期 2015-7-13
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