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By Lai Tsz Chung Kimo


Speaker-independent HMMs (SI HMMs) flatten the probability distribution of features and reduce the accuracy of recognition. This is because of SI HMMs ignore speaker's difference and locate every speaker's data in a global observation space. Yasuo Ariki et al. has proposed a method called "CLAFIC Canonical Correlation Analysis method " (CLAFIC CCA method) which can minimize the distance between two speakers data by projection. In this project, I have implemented this method and have completed several experiments. The result shows the accuracy of CLAFIC CCA has been improved rapidly compare to speaker dependent HMMs (SD HMMs). But compare to SI HMMs, the performance is not as good as expected. 1

Year: 2008
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