Between-Class Covariance Correction For Linear Discriminant Analysis in Language Recognition

  title={Between-Class Covariance Correction For Linear Discriminant Analysis in Language Recognition},
  author={Abhinav Misra and Q. Zhang and Finnian Kelly and John H. L. Hansen},
Linear Discriminant Analysis (LDA) is one of the most widely-used channel compensation techniques in current speaker and language recognition systems. In this study, we propose a technique of Between-Class Covariance Correction (BCC) to improve language recognition performance. This approach builds on the idea of WithinClass Covariance Correction (WCC), which was introduced as a means to compensate for mismatch between different development data-sets in speaker recognition. In BCC, we compute… 

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