A DTW-based probability model for speaker feature analysis and data mining

This paper is a contribution to probabilistic data mining and pattern recognition. A DTW-based statistical model is proposed to explore the subspace structures of speaker feature space for feature evaluation, dimension reduction and inter-class information discovery in pattern space. We demonstrate its usefulness in isolated digits speaker identification, and the performance of the statistical model is compared with standard DTW recognition rate in the experiment. We argue that the probability model can be taken as data mining tools.

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