• Computer Science
  • Published 2001

Extended MLLT for Gaussian Mixture Models

@inproceedings{Olsen2001ExtendedMF,
  title={Extended MLLT for Gaussian Mixture Models},
  author={Peder A. Olsen and Ramesh A. Gopinath},
  year={2001}
}
Prior to publication, please maintain the enclosed paper in confidence and use it only for purposes of evaluating the merit of the proposed paper, and other activities reasonably related to the review process, and please do not make it available, in whole or in part, to the public. The authors thanks IEEE Transactions in Speech and Audio Processing for their courtesy and professionalism in this matter. 

Citations

Publications citing this paper.
SHOWING 1-10 OF 10 CITATIONS

Speech variability compensation for expressive speech synthesis

  • 2013 1st International Conference on Orange Technologies (ICOT)
  • 2013
VIEW 8 EXCERPTS
CITES METHODS
HIGHLY INFLUENCED

Structured precision modelling with Cholesky Basis Superposition for speech recognition

  • 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • 2011
VIEW 4 EXCERPTS
HIGHLY INFLUENCED

Combining temporal and spectral information for Query-by-Example Spoken Term Detection

  • 2014 22nd European Signal Processing Conference (EUSIPCO)
  • 2014
VIEW 1 EXCERPT
CITES METHODS

Rapid adaptation with linear combinations of rank-one matrices

  • 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing
  • 2002

References

Publications referenced by this paper.
SHOWING 1-10 OF 27 REFERENCES

Multiple linear transforms

  • 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221)
  • 2001
VIEW 5 EXCERPTS

Maximum likelihood discriminant feature spaces

  • 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100)
  • 2000

Multivariate Analysis

K. V. Mardia, J. T. Kent, J. M. Bibby
  • Academic Press, New York, 2000.
  • 2000

Semi-tied covariance matrices for hidden Markov models

  • IEEE Trans. Speech and Audio Processing
  • 1999
VIEW 2 EXCERPTS