粒子群演算法應用於語者模型訓練與調適之研究

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姓名

吳昱弘(Yu-hung Wu) 查詢紙本館藏

畢業系所

電機工程學系

論文名稱

粒子群演算法應用於語者模型訓練與調適之研究
(PSO Algorithm for Speaker Model Training and Adaptation)

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摘要(中)

本論文將粒子群演算法應用於語者模型訓練與調適。由於簡單的概念、快速收斂與容易實現，粒子群演算法比基因演算法在處理各式各樣的工程問題上更有效。目前在本論文所使用的粒子群演算法，都是使用沒有改良過的粒子群演算法，原因在於我們的適應函數是用高斯混合的機率密度函數，此函數沒有過於複雜的數學式，所以我們僅使用最原始的粒子群演算法。在傳統的語者確認系統中，模型參數估計大多使用Expectation-maximization (EM) 演算法，在模型收斂過程中，EM演算法要花較多的時間去訓練模型，所以我們提出新的訓練方法，使用粒子群演算法來收斂模型。並從實驗的結果獲得比EM演算法更小的相等錯誤率與決錯成本函數，且其訓練模型的速度也優於EM演算法，確定所提方法的有效性。此外，在做語者模型調適時，平均向量是語者不特定模型最重要的參數，本論文結合粒子群演算法來獲得最佳的平均向量，實驗的結果顯示，本論文所提之方法，比起原本使用的Maximum a Posteriori (MAP) 調適法，可以使語者確認系統的效能提升。

摘要(英)

This thesis introduces the application of Particle swarm optimization (PSO) techniques to speaker model training and adaptation problems. In convention, the Expectation-maximization (EM) algorithm is the dominant approach for model parameter estimation in speaker verification. The experimental results demonstrate that faster convergent rates for training and more accurate rates for speaker verification are obtained using the proposed PSO algorithm as compared to the EM algorithm. In addition, this thesis also utilized proposed the PSO algorithm to adjust the mean parameter in the speaker model adaptation. Experimental results again show that the proposed method outperforms the Maximum a Posteriori (MAP) adaptation in the speaker verification problem.

關鍵字(中)

★ 粒子群演算法
★ 語者模型

關鍵字(英)

★ PSO algorithm
★ Speaker model

論文目次

摘要 I
Abstract II
目錄 III
圖目錄 V
表目錄 VI
第一章緒論 1
1.1研究動機 1
1.2語者辨識概述 2
1.3語者調適概述 4
1.4研究方向 4
1.5文獻探討 5
1.6章節架構 6
第二章語者辨認的基本技術 8
2.1 語音特徵參數擷取 8
2.1.1音框化處理(Framing) 9
2.1.2預強調(Pre-Emphasis) 9
2.1.3加窗處理(Windowing) 10
2.1.4參數抽取 10
2.1.5差量倒頻譜參數 11
2.2 高斯混合模型 12
2.2.1模型描述 12
2.2.2語料多寡與差異對選擇高斯個數的影響 13
第三章粒子群演算法應用於語者模型訓練 15
3.1 簡介 15
3.2 PSO演算法基本公式和模式 15
3.3 慣性權重 16
3.4 PSO演算法最佳化模型參數 21
第四章 Expectation-Maximization演算法 24
4.1 EM演算法 24
第五章 PSO演算法應用於語者模型調適 28
5.1 貝氏調適法 28
5.2 MAP調適法結合PSO演算法 32
第六章實驗與討論 35
6.1 實驗語料 35
6.2效能評估方法 36
6.2.1相等錯誤率(Equal Error Rate, ERR) 36
6.2.2決策成本函數(Decision Cost Function, DCF) 37
6.3 EM與PSO比較 38
6.3.1實驗一 EM演算法最大化平均向量的影響 38
6.3.2實驗二使用PSO演算法找出最佳的平均向量 41
6.4 MAP與MAP-PSO 比較 44
6.4.1實驗三使用PSO演算法在語者模型調適中 44
6.5 EM-MAP與PSO-MAP-PSO 比較 46
6.5.1實驗四使用PSO演算法在語者模型訓練與調適上 46
第七章結論與未來展望 49
7.1結論 49
7.2 未來展望 51
參考文獻 52

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指導教授

莊堯棠(Yau-tarng Juang)

審核日期

2013-7-5

推文