Text-Independent Speaker Verification Using Covariance Modeling

Abstract—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. Index Terms—Covariance modeling, second order statistics, speaker recognition, text independent, utterance level scoring. I