Time-Contrastive Learning Based Deep Bottleneck Features for Text-Dependent Speaker Verification
sTCL
['Feature', 'DNN Lyr.', 'TCL classes', 'Non-target type [%EER/(MinDCF× 100)] Target-', 'Non-target type [%EER/(MinDCF× 100)] Impostor-', 'Non-target type [%EER/(MinDCF× 100)] Impostor-', 'Average']
Similar behaviour to that of N=10 is observed for N=5. This is likely because N=5 and 10 match the underline linguistic structure of utterances in the RSR2015 database so that L4 strongly represents the linguistic information and the network learns good feature representation for speech signal in general at L2. Analysis shows that the minimal, maximal and average number of words per sentence in the database are 4, 8 and 6.3. Average number of frames per utterance is 205, and average number of frames per word is 32.5. This is because small segments resulted from large N values increase the mismatch among segments with the same label. When N=40, the average number of frames per segment is around 5, so it is more likely segments in the same class have different phonetic contents, leading to less-well trained BN-DNN as compared with smaller values of N, e.g. N=10, as well as leading to similar performances between sTCL and uTCL for L2 and L4. On the other hand, clustering helps improve the performance of uTCL-L2 much, by giving decent performances (1.79%, 1.88% and 2.15% for N=10, 20, and 40 respectively).