Full Covariance Modelling for Speech Recognition

@inproceedings{Bell2010FullCM,
  title={Full Covariance Modelling for Speech Recognition},
  author={Peter Bell},
  year={2010}
}
HMM-based systems for Automatic Speech Recognition typically model the acoustic features using mixtures of multivariate Gaussians. In this thesis, we consider the problem of learning a suitable covariance matrix for each Gaussian. A variety of schemes have been proposed for controlling the number of covariance parameters per Gaussian, and studies have shown that in general, the greater the number of parameters used in the models, the better the recognition performance. We therefore investigate… CONTINUE READING
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