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SVM Based Speaker Selection Using GMM Supervector for Rapid Speaker Adaptation

  • Jian Wang
  • Jianjun Lei
  • Jun Guo
  • Zhen Yang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4247)

Abstract

In this paper, we propose a novel method for rapid speaker adaptation called speaker support vector selection (SSVS). By taking gaussian mixture model (GMM) as speaker model, the speakers acoustically close to the test speaker are selected .Different from other selection method, just computing the likelihood between models, we utilizing support vector machines (SVM) to obtain a ‘more optimal speaker subset’. Such selection is dynamically determined according to the distribution of reference speakers close the test. Furthermore, a single-pass re-estimation procedure conditioned on the selected speakers is shown. This adaptation strategy was evaluated in a large vocabulary speech recognition task. The presented method improves the relative accuracy rates by 13% compared to the baseline system.

Keywords

Support Vector Machine Hide Markov Model Gaussian Mixture Model Speaker Identification Hide Markov Model Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jian Wang
    • 1
  • Jianjun Lei
    • 1
  • Jun Guo
    • 1
  • Zhen Yang
    • 1
  1. 1.School of Information EngineeringBeijing University of Posts and TelecommunicationsBeijingChina

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