Improving speaker discrimination of target speech extraction with time-domain SpeakerBeam
Table 1: Amount of data and number of female (F) and male (M) speakers.
['[EMPTY]', 'Train #Mixtures', 'Train #Spks', 'Train #F', 'Train #M', 'Test #Mixtures', 'Test #Spks', 'Test #F', 'Test #M']
We performed experiments using two datasets, multi-channel WSJ0 2 mixtures (MC-WSJ0-2 mix) and CSJ-2mix. The curves were obtained by creating 3 different training sets with 100, 500 and all 937 training speakers. In all cases, we used 50k mixtures. Interestingly, we observe that increasing the number of speakers has little effect on TasNet performance, but greatly improved the performance of SpeakerBeam. This suggests that, for SpeakerBeam, separating signals is somewhat easier than identifying speakers. The SI-loss provided consistent improvement when using more than 100 speakers (This is why we did not use the SI-loss in the MC-WSJ0 experiments).