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

  • Authors:
  • Jingwei Liu;Qiansheng Cheng;Zhongguo Zheng;Minping Qian

  • Affiliations:
  • Department of Information Science, School of Mathematical Science, Peking University, Beijing 100871, China;Department of Information Science, School of Mathematical Science, Peking University, Beijing 100871, China;Department of Information Science, School of Mathematical Science, Peking University, Beijing 100871, China;Department of Information Science, School of Mathematical Science, Peking University, Beijing 100871, China

  • Venue:
  • Pattern Recognition Letters
  • Year:
  • 2002

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Abstract

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.