Incorporation of Speech Duration Information in Score Fusion of Speaker Recognition Systems
TABLE II: Speaker identification errors and speaker verification equal error rates (EER) are shown for all subsystems and score fusion methods. Results with duration-based score fusion are also reported where applicable. Systems with best performance for identification and verification tests are shown as bold. For identification and verification experiments we have 2625×81 and 2625×81×152 tests respectively. Distance between upper 95% confidence interval and error mean for identification and verification tests are 0.1% and 0.02% respectively.
['[EMPTY]', '[EMPTY]', 'Subsystems GMM', 'Subsystems TVS-cosine', 'Subsystems TVS-PLDA', 'Score fusion mean', 'Score fusion LR', 'Score fusion NN']
Two sets of experiments were done on the test data. In the first set of experiments, all 81 sets of data were combined and accuracy of each subsystem as well as the results for score fusion using mean, LR, and NN were calculated. Score fusion models were trained using the development data. In the second experiment, LR and NN score fusion models were trained on each enrollment and development duration condition separately, creating 81 model for each system. Accordingly, the tests were done using the models trained with the corresponding enrollment and test duration, and accuracies were reported on the whole data. It is important to note that the high error rates in this table are caused by the very short tests durations (0.1, 0.2, 0.5, and 1 seconds). In fact, experiments with very short durations on at least one side of enrollment or test cover 69% of the total experiments. One might expect that training a score fusion model for each duration condition will reduce the performance of the fused scores by limiting the amount of training data.