John R. Kender, Rick Kjeldsen
IEEE Transactions on Pattern Analysis and Machine Intelligence
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.
John R. Kender, Rick Kjeldsen
IEEE Transactions on Pattern Analysis and Machine Intelligence
Corneliu Constantinescu
SPIE Optical Engineering + Applications 2009
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Photomask and Next-Generation Lithography Mask Technology 2004
David W. Jacobs, Daphna Weinshall, et al.
IEEE Transactions on Pattern Analysis and Machine Intelligence