Abstract
This thesis discusses the research on text-dependent speaker identification. Two parts are included in this thesis: "improvement on endpoint detection" and "classifier combination". "Improvement on endpoint detection" refers to improvement upon the accuracy of the current method in endpoint detection. We propose two methods to improve the accuracy: automatic removal of the noisy segments at the beginning and the end of the sentence and adjustment on computing distance for dynamic time warping (DTW) algorithm. The first method collects the information about these noisy segments and finds the best parameters to improve the accuracy of endpoint detection. On the other hand, preliminary experimental results show that most of the noise-affected cases occur at the end of the utterance. We therefore use the second method to adjust the DTW algorithm to alleviate the sentence-end noise problem. We also propose a classifier combination method to further improve the accuracy of speaker identification. This method adopts multiple classifiers at the same time, where DTW, due to its high recognition performance, is used as the first stage classifier. The distance value computed from the DTW algorithm is then checked against a threshold value. If the result is a reject, i.e. the distance is higher than the threshold, we hand the utterance over to the next stage classifier; if the result is an acceptance, i.e. the distance is lower than the threshold, we can conclude that a close match is found and accept the recognition result without further classification. The optimal threshold that decides an acceptance or a reject is selected empirically from various reference utterances through exhaustive search. In this thesis, we use 39-dimentional Mel frequency cepstral coefficients (MFCCs) as our features. And we assess the validity of the classifier combination system by comparing the recognition rate between the proposed system and the original system using DTW. The experimental result has shown that classifier combination is better than the original system.