Time-Contrastive Learning Based Deep Bottleneck Features for Text-Dependent Speaker Verification
TABLE VI: TD-SV results for the score-level fusion of MFCCs and BN features on the m-part-01 task of the RedDots database using the GMM-UBM method
['Score fusion (# [ITALIC] no. [ITALIC] ofclasses)', 'Non-target type [%EER/(MinDCF× 100)] Target-wrong', 'Non-target type [%EER/(MinDCF× 100)] Impostor-correct', 'Non-target type [%EER/(MinDCF× 100)] Impostor-wrong', 'Average EER/MinDCF', 'Without fusion Avg.EER/MinDCF']
Scores of the different systems are combined with weights as follows. First, the inverse of the mean EER value (mieer) of each system i is calculated. Second, inverse values are scaled so that the summation of the weights (wi for the ith system) become unity. Finally the fusion score is the weighted sum of component system scores. The steps are detailed in the following equations. yi = 1mieer (7) wi = yi∑li=1yi (8) fusedscore = l∑i=1wi∗scoresysi , it is noticed that all fusion systems perform better than MFCCs alone. When combined with MFCCs, all BN features obtain better performance compared to their standalone counterparts. This shows that BN features carry information complementary to MFCCs when used for TD-SV. uTCL-BN with clustering still gives the best performance followed by PHN-BN3.