本論文主要的研究目標是將關鍵詞萃取與語者辨識結合在一起，由於本實驗室在語者辨識方面的研究已經累積了不少成果，所以主要著重在如何提升關鍵詞萃取的辨識能力以及對於辨識速度如何加速為其研究重點。
由於在關鍵詞模組與無關鍵詞模組的組合中，無關詞模組對辨識率有很大的影響，所以我們嘗試改變無關詞模組的形式，去找出一組較佳的無關詞模組來進行辨識，經由實驗驗證所選取的無關詞模組，由於較省記憶空間所以辨識速度也跟著提升，再者進行關鍵詞萃取時我們利用雙重辨識架構進行關鍵詞的萃取，第一層利用一階動態演算找出Top N最接近的候選者，第二層則將Top N的候選者跟經由訓練得到的Top N的信任分數臨界值作判斷，假如該名次被拒絕掉則名次由下一名遞補，以此類推，之後我們找最佳名次當作萃取出的關鍵詞。我們經由實驗驗證這個方法確實可提高整體的辨識率。在確認上，我們使用了一個不用訓練每個次音節臨界值的方法，使得以後建立確認系統可以更快速。

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