Within-class covariance normalization for SVM-based speaker recognition

@inproceedings{Hatch2006WithinclassCN,
  title={Within-class covariance normalization for SVM-based speaker recognition},
  author={Andrew O. Hatch and Sachin S. Kajarekar and Andreas Stolcke},
  booktitle={INTERSPEECH},
  year={2006}
}
This paper extends the within-class covariance normalization (WCCN) technique described in [1, 2] for training generalized linear kernels. We describe a practical procedure for applying WCCN to an SVM-based speaker recognition system where the input feature vectors reside in a high-dimensional space. Our approach involves using principal component analysis (PCA) to split the original feature space into two subspaces: a low-dimensional “PCA space” and a high-dimensional “PCA-complement space… CONTINUE READING

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Key Quantitative Results

  • When applied to a state-of-the-art MLLR-SVM speaker recognition system, this approach achieves improvements of up to 22% in EER and 28% in minimum decision cost function (DCF) over our previous baseline.

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References

Publications referenced by this paper.
SHOWING 1-10 OF 13 REFERENCES

Improvements in MLLR-Transform-based Speaker Recognition

  • 2006 IEEE Odyssey - The Speaker and Language Recognition Workshop
  • 2006
VIEW 7 EXCERPTS

Four weightings and a fusion: a cepstral-SVM system for speaker recognition

  • IEEE Workshop on Automatic Speech Recognition and Understanding, 2005.
  • 2005
VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL

Generalized Linear Kernels for One-Versus-All Classification: Application to Speaker Recognition

  • 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings
  • 2006
VIEW 6 EXCERPTS

Advances in channel compensation for SVM speaker recognition

  • Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005.
  • 2005
VIEW 2 EXCERPTS