本論文主要在討論如何將不精確的音節辨識器，應用在全文資訊檢索以及語句辨識上。此音節辨識器做的是「沒有音調的音節辨識」，並且沒有使用文法結構來調整辨識出來的音節字串。
若將此音節辨識器拿來應用，要面對的困難有二，而本論文也提出相對應的解決方法：

第一、音節容易辨識錯誤。

雖然音節辨識錯誤，但是辨識錯的音節仍有其參考價值，因此我們制定了「音節相似程度表」 描述音節之間的關聯，以提高系統容忍度。

第二、解碼出來的音節個數不如預期。

也就是會產生「刪除性錯誤」以及「嵌入性錯誤」。對於此問題的解決方案，因應用不同而異。在語句辨識上我們使用「動態時間扭曲法」，使得字串的比對更具彈性﹔在全文資訊檢索上，則使用類似交集的方法並搭配權重來計算各個文件的分數。

In this thesis, we discuss the methods for constructing a Mandarin syllable-recognizer which can be used for diverse applications such as "full-text information retrieval" and "sentence recognition". The syllable-recognizer performs recognition tasks without using tone information and language models, leading to a low recognition rate at the syllable level. Hence we identify the associated problems and propose methods to deal these problems.
The first problem is the low recognition rate at the syllable-level. The identified syllable might not be correct, but it bears similarity to the intended syllable. Therefore, we establish a "syllable-similarity table" to describe the similarity between any two syllables, and the similarity scores are used in ranking the possible output. The system's performance becomes more robust after adding this enhancement.

Another problem is the number of syllables this recognizer decoded may not be correct. The most common errors are "deletion error" and "insertion error". Different applications call for different strategies to deal with the problem. In sentence recognition, we apply the concept of "dynamic time warping" to make the string-matching process more flexible. In full-text information retrieval, we use the methods of "syllable intersection" and "syllable weights" to evaluate the score of each retrieved document.

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