Deep Factorization for Speech Signal
Table 2: Accuracy (ACC) and macro average precision (MAP) of the AER systems.
['[EMPTY]', 'Training set ACC% (fr.)', 'Training set MAP% (fr.)', 'Training set ACC% (utt.)', 'Training set MAP% (utt.)']
and the utterance-level (utt.) are reported. It can be seen that with the conditional factors involved, the ACC and MAP values on the training set are significantly improved, and the speaker factor seems to provide more contribution. This improvement on training accuracy demonstrates that with the conditional factors considered, the speech signal can be explained much better. Again, we observe a clear advantage with the CDF training. Note that involving the two factors does not improve the utterance-level results. This should be attributed to the fact that the DNN models are trained using frame-level data, so may be not fully consistent with the metric of the utterance-level test. Nevertheless, the superiority of the multiple conditional factors can be seen clearly from the frame-level metrics. We note that the discrepancy between the results on the training set and the evaluation set is not due to over-fitting caused by the CDF approach; it simply reflects the mismatch between the training set and evaluation set, hence the difficulty of the task itself. Hz data.