| Summary: | 碩士 === 國立中央大學 === 電機工程研究所 === 95 === In speaker recognition, it is important to have effective training data to train speaker models which have a great effect on recognition performance. In abundant training data, traditional speaker models which is based on maximum likelihood have a good effect, but it is opposite in slight training data. Besides, being independent with other speakers, we used training data for the same speaker to train speaker model owning to the method of maximum likelihood. In the stage of training model, we did not concern the relation of different speaker model, so we would get confused easily in speaker recognition. In recent years, Discriminative Acoustic Model Training is proposed to minimize classification error, not maximizing training acoustic models likelihood.
In this thesis, we use minimum classification error to train speaker models, and support vector machines to improve minimum classification error. Due to the non-robustness of minimum classification error in setup for the amount of competitive speakers, we use the scores of speaker models for training data as labels of classes to train support vector machines. Then, we use support vectors to choose competitive speakers to make more robust and higher speaker recognition performance than minimum classification error.
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