在本論文中著重於語者確認後端的研究，當有了測試語料後，希望能對該測試語料做到最佳的辨識效能，因此主要的研究方向為測試語音與各註冊語者模型的處理。首先系統採用正規化計分方式，並加入粒子群演算法來優化模型參數，粒子群演算法是一種最佳化演算法，透過模擬鳥群或魚群搜索食物的方式來找尋最佳解，屬於群體智慧的方法，其粒子具有記憶性，且該演算法計算簡單與快速收斂，故將其應用於語者確認語料的建模上，藉由其優化的特性以建立更加精確的語者模型，使得系統更具有鑑別力。再者，本論文將簡單線性迴歸分析應用於語者確認系統中，簡單線性迴歸分析是統計學裡重要的分析方法，常用來分析資料之間的相關性，此處將語者確認結果建立簡單線性迴歸模型，透過普通最小平方法的估計，及判定係數的分析，對語者確認的結果做結合，使得系統對測試語音的辨識更加精準，進而提升系統的辨識效能。;This thesis focused on speaker verification between test corpus and registered speaker models. First of all, the thesis introduces score normalization approaches to the speaker verification system. Then, we apply Particle Swarm Optimization algorithm to optimize model parameters. The main idea of PSO method is like fish foraging behavior. All particles of PSO have memories. The algorithm has simple calculation and fast convergence. With its optimized features to build a more accurate speaker model, the system is more discernment. In addition, the thesis also introduces a regression analysis method to speaker verification system. Regression analysis is a useful statistics analysis method. We build the regression model for each speaker by ordinary least squares estimation and the coefficients of determination analysis. Experiments showed that the proposed method can improve performance of the speaker verification system.
