Discriminative Neural Clustering for Speaker Diarisation
Table 2: ser for different data augmentation techniques for sub-meetings of length 50 segments.
['randomisation', 'w/o daug', 'w/ daug']
The use of random sub-sequences (sub-meetings) (Sec. For the dev set (used as the validation set), this augmentation technique is applied exclusively. Per original meeting, which dictates the label sequence y1:N, 5000 sub-meetings with 50 segments are generated and augmented using the techniques of Sec. Full meetings of the eval set are split into sub-meetings with at most 50 segments. The trend is different after applying \glsdaug (\nth2 col. Using none achieves an \glsser of 15.25%. The result for meeting is reduced to 13.57%. This shows that \glsdaug generates fairly typical data. Sec. The number of speaker groups available from global (∼C1554) is larger than the number of sub-meetings. Thus, \glsdaug does not increase the number of speaker groups seen by the \glsdnc model when using global.