Speaker Recognition with Random Digit Strings Using Uncertainty Normalized HMM-based i-vectors
TABLE III: Combinations of Uncertainty Normalization, Regularized LDA, S-Norm and PLDA
['Model', 'Version', '[BOLD] Male EER [%]', '[BOLD] Male NDCFminold', '[BOLD] Male NDCFminnew', '[BOLD] Female EER [%]', '[BOLD] Female NDCFminold', '[BOLD] Female NDCFminnew']
First of all, we observe that the contribution of Regularized LDA is rather minor compared to uncertainty normalization. This result is rather surprising; it shows that state-of-the-art performance can be attained even without explicit channel modelling, i.e. without the need of collecting multiple training recording coming from different channels, sessions or handsets, per speaker.