Within-class covariance normalization and cos-score for speaker verification

The eigenvoice-based speaker verification method can compensate for voice mismatch in text-independent speaker verification applications,but it does not compensate channel mismatch,which also exerts a negative impact on the verification.Therefore channel mismatch compensation based on eigenvoice method was proposed.First,eigenvoice was adopted to compensate voice mismatch,then WCCN was applied to compensate channel mismatch.After these compensations,the speaker factor was computed and acted as speaker model.Based on the speaker factor model,Cos-score calculation was conveniently used to test verification operation.The experiment results show better performance,with an improvement by 22.85% at EER and 31.22% at MinDCF,while compared with GMM-UBM-SVM,an improvement was achieved by 9.14% at MinDCF.Meanwhile,the new method needs less storage space,which benefits practical applications.