End-to-End Neural Speaker Diarization with Permutation-Free Objectives
Table 1: Effect of loss functions evaluated on simulated speech generated with β=2. The models are trained using 10,000 mixtures genarated with β=2.
['PIT loss', 'DPCL loss', 'DER (%)']
We evaluated the effect of the proposed loss functions. With PIT and DPCL losses, we set the mixing parameter α=0.5. It is observed that PIT loss is essential for training of our neural network. It also demonstrates that DPCL loss helps improve performance.