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Towards the use of full covariance models for missing data speaker recognition | IEEE Conference Publication | IEEE Xplore

Towards the use of full covariance models for missing data speaker recognition


Abstract:

This work investigates the use of missing data techniques for noise robust speaker identification. Most previous work in this field relies on the diagonal covariance assu...Show More

Abstract:

This work investigates the use of missing data techniques for noise robust speaker identification. Most previous work in this field relies on the diagonal covariance assumption in modeling speaker specific characteristics via Gaussian mixture models. This paper proposes the use of full covariance models that can capture linear correlations among feature components. This is of importance for missing data marginalization techniques as they depend on spectral rather than cepstral feature representations. Bounded and complete marginalization schemes are investigated both with diagonal and full covariance mixture models. Speaker identification experiments using stationary and non-stationary noise confirm that full covariance models are indeed superior compared to diagonal models.
Date of Conference: 31 March 2008 - 04 April 2008
Date Added to IEEE Xplore: 12 May 2008
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Conference Location: Las Vegas, NV, USA

References

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