Towards the use of full covariance models for missing data speaker recognition
Marco Kühne, Daniel Pullella, Roberto B. Togneri, Sven Erik Nordholm · IEEE International Conference on Acoustics Speech and Signal Processing · 2008
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.