End-to-End Neural Speaker Diarization with Permutation-Free Objectives
Table 2: Effect of the number of training mixtures evaluated on simulated speech generated with β=2. The models are trained with β=2.
['Number of training mixtures', 'DER(%)']
It is observed that increasing the number of training samples improves the performance. Because our proposed method can be trained with any speech mixture with corresponding time annotations, it is possible to utilize large scale speech corpora for improving robustness of the system.