Abstract
This thesis focuses on improving in the performance of a Taiwanese keyword spotting system by integrating speech assessment and pitch contour classification. In the first part of this research, we use different methods to implement a Taiwanese keyword spotting system. In second part, we improve the system by validation using speech assessment and pitch contour classification.In the first part, two methods are adopted to implement the keyword spotting system. The first method uses the hidden Markov model while the second method uses the phone mismatching method. The phone mismatching method can be further characterized into three types of algorithm: penalty matrix (PM), confusion matrix (CM) and Levenshtein matrix (LD). We then perform speech assessment and pitch contour classification to validates the candidate keywords selected by these two methods to refine the results. A threshold is used for each of these two methods, and a decision tree is used to make the final decision.Experimental results shows that the HMM method can achieve an equal error rate (ERR) of 46.5%. The ERR reduces to 26.5% after the HMM method is incorporated with speech assessment validation, the FAR further reduces to 24.7% after being incorporated with pitch contour classification. In the phone mismatch experiment, PM, CM, and LD achieve an ERR of 39.4%, 34.0%, and 42.2% respectively. After being incorporated speech assessment validation, ERRs reduce to 34.6% for PM and 28.4% for CM. After being incorporated with pitch contour classification, ERRs further reduce to 33.7% for PM and 27.3% for CM. This concludes that the validation technique using speech assessment and pitch contour classification can improve the performance of Taiwanese keyword spotting.