| Summary: | 碩士 === 國立交通大學 === 電信工程系 === 88 === In this thesis, the context-dependent initial-final models are used to construct both the keywords and filler models in order to improving the performance of Mandarin keyword spotting system. The decision tree clustering method is used to find the right-context-dependent final models used in the system. 100 final-dependent initial models and 290 right-context-dependent final models are first constructed for the keywords. The system performance is examined when different numbers of filler models which can be found from the clustering procedure, are used in the system. In order to increasing the keyword recognition rate, the Top-N recognition result of the input sentential utterance is first found, and a verification procedure is used to filter out the candidates with lower keyword-filler likelihood ratio and shorter syllable duration. Finally, the normalized keyword recognition scores and lengthening factor are combined to find the best sentential candidate. The performance of above methods is examined in the telephone number inquiry system with 1013 keywords, the 86.4% keyword recognition rate is achieved with 0.37 FA/KW/HR false alarm rate.
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