Robert Manson Sawko, Malgorzata Zimon
SIAM/ASA JUQ
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.
Robert Manson Sawko, Malgorzata Zimon
SIAM/ASA JUQ
John R. Kender, Rick Kjeldsen
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hans Becker, Frank Schmidt, et al.
Photomask and Next-Generation Lithography Mask Technology 2004
Jonathan Ashley, Brian Marcus, et al.
Ergodic Theory and Dynamical Systems