本篇論文提出了新穎的加強式跨字參考模板，並將其應用到語者獨立孤立詞的語音辨識系統。加強式的跨字參考模板是由一群模板中所產生出來的。其主要的產生步驟有兩個，分別是動態時間校準(Dynamic Time Warping)的配對，還有算術平均。因為每個模板之間的長度都不盡相同，所以並不能直接平均。在使用平均運算之前，必須先讓每個模板的長度一致。所以我們就採用了動態時間校準來解決這個問題。在配對完成之後，就可以進行平均運算，產生一個加強式的跨字參考模板。
軟體實現的實驗結果指出本篇論文所提出的系統使用線性預測倒頻譜係數(Linear Prediction Cepstral Coefficients)作為特徵，可以在30個命令語句的環境下，可高達到98.83%的辨識率。而使用跨字參考模板以及一般的參考模板所得到的辨識率分別為97.58%和93.58%。使用加強式跨字參考模板的辨識率明顯高於使用其他兩種模板。此外，硬體實現的實驗結果指出不論是內部測試或外部測試，平均辨識率都高於90%。這樣的實驗結果證實了我們所提出之想法的有效性。

In this study, the novel enhanced cross-words reference templates (ECWRTs) are proposed and applied to speaker-independent isolated word recognition. The ECWRT is a reference template generated from a set of templates. The main procedures of ECWRT generation are DTW-matching and average operations. Due to the variance of lengths of templates, the average operations cannot perform directly. To solve this problem, dynamic time warping (DTW) is used. After DTW-matching, the matched frames of templates are averaged to form the ECWRT. The experimental results of software implementation show that the proposed system with linear prediction cepstral coefficients (LPCCs) for 30-word vocabulary can achieve an average accuracy rate of 98.83%. Such a recognition rate is higher than 97.58% and 93.58% using CWRTs and conventional reference templates, respectively. Moreover, the experimental results of hardware implementation indicate that the average recognition rates for the inside and outside test are higher than 90%. The experimental results demonstrate the effectiveness of the proposed idea.

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