End-to-End Neural Speaker Diarization with Permutation-Free Objectives
Table 3: Detailed DERs (%) evaluated on simulated speech generated with the β=2. MI, FA and CF denote miss, false alarm and confusion error rates, respectively. The proposed model is trained using 100,000 mixtures generated with β=2.
['Method', 'DER', 'MI', 'FA', 'CF']
We compared the proposed method with the baseline systems using the simulated speech mixtures. It is observed that miss rate is dominant in the DER of the baseline systems, due to the lack of capability for overlapping speech. In contrast, the proposed method achieved significantly low miss rate. The results indicate that the proposed method successfully detects overlapping segments as well as single-speaker segments and silence segments. Regarding the confusion error rate, the proposed method is better than the i-vector system, while it is worse than the x-vector system. For reducing the confusion errors, it is possible to use data augmentation for learning noise and speaker variations, which is utilized in the x-vector system.