由於辨識錯誤是語音辨識系統無法避免的問題，關鍵詞驗證技術成為語音辨識應用上必要的一環。而支撐向量機經由決策邊界的最佳化，在模型辨識上已被證實是具有良好效能的分類器。本論文的研究是先用支撐向量機做聲韻母驗證，並進一步利用聲韻母分類器的輸出，對關鍵詞進行驗證。這樣的做法不需要對個別關鍵詞去訓練反關鍵詞模型或分類器，可以使關鍵詞擷取系統具有較佳的擴充性，並易於移植到不同的應用領域。論文中我們比較了聲學分數、時長、反模型距離、參考路徑距離等驗證特徵的鑑別力，並藉由結合不同的輸入特徵來提升驗證效能。進一步，我們比較了不同的核心函數，並對其支撐向量機參數進行最佳化。實驗結果顯示，在關鍵詞驗證上，支撐向量機使用線性核心函數時相等錯誤率可達到23.61%，比事先計算最佳化門檻值的38.61%和類神經網路的26.77% 均為佳；若再加上詞長相關的門檻值，可以讓相等錯誤率進一步的下降到19.33%。

Due to recognition error is an avoidless problem in the speech recognition, keyword spotting becomes a import role. Support Vector Machines via a decision boundary optimization, has proved is an excellent classifier in the model recognition. In our research, we first use Support Vector Machines in initial-final’s verification, and then do verification in keyword level with combination of phone level classifiers output. Because we don’t training keyword models or keyword classifiers, so our keyword verification system has a better portability and can easily change application domain.
In the paper, we propose four verification features, acoustic score, duration, anti-model distance and reference path distance. We first compare feature’s discrimination, and then combine them to get a better performance. We also compare kernel function’s performance, and adjust optimal parameters to Support Vector Machines. Experimental results show that using Support Vector Machines with linear kernel function, equal error rate is 23.61%, better than reference verification system 38.61% and Neural Network classifier 26.77%. Finally we use length dependent threshold can further decrease equal error rate to 19.33%.

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