由於時代的進步，人類與機械的互動機會越來越多，故本研究架構一語者辨識系統，在去除辨識子句內容及時間長短的限制條件下判斷講者身分，並以遞迴學習機制降低誤判發生率，使講者免於記誦複雜的通關密語。
本系統依流程主要分為三部分：語音訊號前處理、特徵資料分群和辨識演算及遞迴學習，辨識系統主要以小波轉換及隱藏馬可夫模型作為主要演算依據，並以最近鄰居選擇、中心點分離及K群平均分群演算法配合辨識機制中的訓練演算流程建立語者特徵資料庫，在每次辨識時，記錄最佳的語者特徵值參數，回傳給資料庫作更新，以達到機器學習的功能。
辨識結果顯示，以小波轉換架構的辨識演算法，其辨識率可達80%以上，以隱藏馬可夫模型的機率狀態轉移進行辨識，其辨識率可達70%以上。

With the current progress of technological development, there is an increasing trend of human and machine interactions. This research builds a framework for speaker identification system. This system ignores the constraint of the content within the spoken sentences and the length of speaking time, which allows users no longer for memorizing lengthy or complicated passwords for identification. Besides, recursive learning process is used as well to decrease the failure rate.
This speaker identification system can be divided into three main parts, which are phonic signal preprocessing, feature extraction by clustering and identification algorithm with recursive learning according to algorithmic diagram. The main algorithms of the identification system are wavelet transform method and Hidden Markov Model. By using Nearest Neighbor Selection Rule, Centroid Splitting Algorithm and K-means Algorithm cluster the characteristics of voice to construct speaker characteristic database, which updates its date regularly by receiving the better characteristic written when any recognition has been done.
The recognition results show that using wavelet transform method an identification rate of eighty percent or above can be achieved, while with Hidden Markov Model Transition States an identification rate of seventy percent or above can be achieved.

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