在語者調適的方法中，如何在有限語料的情況下，對辨識率進行有效的提升，一直都是重要的議題。本論文探討了一些以參考語者為基礎的語者調適方法，在只需使用者的少量語料的情況下也能達到好的辨識率。所謂的參考語者是指語料庫裡與目標語者統計特性相近的語者。我們探討的方法包括：直接使用相近的語料進行調適、用參考語者模型做加權組合、以及參考語者特徵轉換等三種方式。進一步，我們對「參考語者特徵轉換」調適法做了修正，加入了目標語者的MLLR_MAP調適模型以改善辨識率。最後，我們結合了特徵參數轉換與模型加權組合這兩種調適法，並討論其對於辨識率的影響。
　由實驗結果得知，在「參考語者特徵轉換」調適法中加入目標語者的調適模型，最多可對於語者不特定模型的辨識率提升約6.67%，對於改善辨識率可以有明顯的提升。而在調整變異數對實驗結果的影響方面，根據實驗結果顯示，在模型重估時使用原來的變異數而不重新調整的話，通常可以有較佳的辨識率。最後在結合「參考語者特徵轉換」與「參考語者模型加權」這兩種調適法的方法中，實驗結果顯示比起原本只使用與目標語者相近的參考語者做加權組合，其實驗結果會有較佳的辨識率。

How to effectively improve the recognition rate through speaker adaptation has been an important issue for speaker recognition system. In this paper we discuss some speaker adaptation methods based on reference speakers, including finding the speeches or speakers in the database that are close to the target speaker, the acoustic models of the reference speakers, and transforming the features of reference speakers during re-estimation. All these basic methods can achieve improvements on adaptation performances. In addition, we modify and combine the above methods to obtain new adaptation schemes, and achieve better performance than MAP, MLLR methods.
In the experiment results, compared to speaker independent model, the best obtainable performance is 6.67% based on the feature transformation. Also, when integrating model combination approach and feature transformation approach for reference speakers, a better performance can be achieved. It can be concluded that, it is potentially helpful to utilize the data or models of reference speakers for improving adaptation performance provided the reference speakers are close enough to the target speaker.

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