Human and Machine Speaker Recognition Based on Short Trivial Events
Table 4: EER results on CSLT-TRIVIAL-I with the i-vector and d-vector systems.
['Systems', 'Metric', 'EER% Cough', 'EER% Laugh', 'EER% ‘Hmm’', 'EER% ‘Tsk-tsk’', 'EER% ‘Ahem’', 'EER% Sniff']
For the machine test, there are about 260,000 trials for each event type. It can be observed that the d-vector system outperforms the i-vector system by a large margin, confirming that the deep speaker feature learning approach is more suitable than the statistical model approach when recognizing short speech segments. Comparing different events, it can be found that ‘hmm’ conveys the most speaker information, and cough, laugh, ‘ahem’ are less informative. ‘ Tsk-tsk’ and Sniff are the least discriminative. All these observations are consistent with the results of the human test. Moreover, we found that for d-vector systems, the discriminative normalization approaches, LDA and PLDA, did not provide clear advantage on ’hmm’ and sniff. A possible reason is that there is little intra-speaker variances involved in these two types of events, so the statistical based discrimination is not helpful.