Lower and upper bounds for approximation of the Kullback-Leibler divergence between Gaussian Mixture Models

@article{Durrieu2012LowerAU,
  title={Lower and upper bounds for approximation of the Kullback-Leibler divergence between Gaussian Mixture Models},
  author={Jean-Louis Durrieu and J. Thiran and Finnian Kelly},
  journal={2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  year={2012},
  pages={4833-4836}
}
Many speech technology systems rely on Gaussian Mixture Models (GMMs). The need for a comparison between two GMMs arises in applications such as speaker verification, model selection or parameter estimation. For this purpose, the Kullback-Leibler (KL) divergence is often used. However, since there is no closed form expression to compute it, it can only be approximated. We propose lower and upper bounds for the KL divergence, which lead to a new approximation and interesting insights into… Expand
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