Abstract
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.