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Text-independent speaker verification using covariance modeling

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1 Author(s)
Zilca, Ran D. ; Div. of Res. & Dev., AMdocs, Raanana, Israel

This letter describes speaker verification using a covariance modeling approach for speaker and world modeling. Two verification methods are suggested: frame level scoring and utterance level scoring. Both methods exhibit extremely low computational and model-storage requirements. The suggested methods are tested on the male segment of the 1999 NIST Speaker Recognition Evaluation corpus, using a single training session, and compared to a Gaussian mixture model (GMM) system. The degradation in accuracy and the computational requirements are estimated. Covariance modeling is seen to be a viable alternative to GMM whenever computational and storage requirements must to be traded with verification accuracy.

Published in:

Signal Processing Letters, IEEE  (Volume:8 ,  Issue: 4 )