本論文主要針對文本無關之語者驗證方法進行研究，提出效能改進的方式，期望能夠找出具實用價值的模型訓練流程與驗證架構。語者驗證系統效能主要會受到三個因素的影響，分別是語者特徵參數、語者模型與訓練流程，以及決策模組。本論文分別針對語者模型訓練流程、背景語者挑選方法、性別相關背景模型、和基於類神經網路的決策模組分別進行實驗。結果顯示，對於基本的語者驗證系統，增加高斯混合模型的混合數可以有效提升驗證效能，迭代次數則無明顯影響。在背景語者挑選方法上，以Bhattacharyya距離或最大相似度去挑選背景語者均優於隨機挑選，最大相似度則比Bhattacharyya距離更佳，但其計算量較大。另外，只挑選同性別的背景語者會比不限性別得到更好的驗證效能。背景語者挑選可在小幅犧牲驗證效能下減少許多相似度計算。而在類神經網路分類器的決策模組方面，不論是使用特定語者或是通用語者分類器，均能有效地提升驗證效能。改進原因是類神經網路的非線性決策函數具有較佳調整能力。而特定語者分類器改進幅度較大，然而其代價為使用者必須要錄製更多的語音資料作為訓練分類器之用。

This thesis investigates text-independent speaker verification method, and proposes a few ways of improving the verification performance by using better training procedure, selection of background speakers, gender-dependent background model, and nonlinear decision module based on artificial neural network. All these efforts aim to improve the speaker verification method such that it can be applied to a practical application.
Experimental results show that the number of mixtures of GMMs influences the verification performance largely, while the number of iterations is less relevant. The selection of background speakers can reduce the computation cost effectively by degrading the performance slightly. In addition, the selection of background speakers based on Bhattacharyya distance or maximum likelihood is significantly better than random selection, and gender-dependent background model is shown to achieve better performance than gender-independent one. As regards to the decisions module, both the speaker-dependent and speaker-independent ANN classifiers can improve the verification performance effectively. This is because nonlinear decision function of neural network has better descriptive capability.

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