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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Title:	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
Authors:	吳昱弘;Wu,Yu-hung
Contributors:	電機工程學系
Keywords:	粒子群演算法;語者模型;PSO algorithm;Speaker model
Date:	2013-07-05
Issue Date:	2013-08-22 12:13:42 (UTC+8)
Publisher:	國立中央大學
Abstract:	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。 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.
Appears in Collections:	[Graduate Institute of Electrical Engineering] Electronic Thesis & Dissertation

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