Eigenvalues Driven Gaussian Selection in continuous speech recognition using HMMs with full covariance matrices

@article{Janev2008EigenvaluesDG,
  title={Eigenvalues Driven Gaussian Selection in continuous speech recognition using HMMs with full covariance matrices},
  author={Marko Janev and Darko Pekar and Niksa Jakovljevic and Vlado Delic},
  journal={Applied Intelligence},
  year={2008},
  volume={33},
  pages={107-116}
}
In this paper a novel algorithm for Gaussian Selection (GS) of mixtures used in a continuous speech recognition system is presented. The system is based on hidden Markov models (HMM), using Gaussian mixtures with full covariance matrices as output distributions. The purpose of Gaussian selection is to increase the speed of a speech recognition system, without degrading the recognition accuracy. The basic idea is to form hyper-mixtures by clustering close mixtures into a single group by means of… CONTINUE READING
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