摘要
本篇論文重點在於語音信號分析處理這部分，根據黃鍔等人發表了一個新的資料處理方法—經驗模態分解法，這個方法利用系統變化的內部時間尺度來作為能量的直接析出，可將資料表達成內建模態函數，而這些函數即是原輸入訊號的基底，其具有完整性、幾乎正交性及可適性。可適性可表達原函數之物理特性，藉以處理非線性及非穩態性時間序列問題。因為這種方法的特性，再加上語音訊號也是非線性時間序列，而且瞭解說話內容文字特性及說話人的特性將有助於語音辨識，所以基底能夠表達原輸入訊號之物理特性將更加幫助我們作語音模型的訓練。是故改善傳統訊號分析方式，使訊號呈現其特性，為本研究之一大課題。
本論文利用經驗模態分解法找出與文字特性較有關的輸入基底，以訓練一套模型，以在辨識流程上求得較好的辨識率。

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