Discriminative Neural Clustering for Speaker Diarisation
Table 3: Comparison of dnc vs. sc at different cl stages. Finetuned dnc models are initialised from the corresponding models using full data augmentation.
['(sub-)meeting length #segments', '(sub-)meeting length duration', 'dnc data aug.', 'dnc finetune', 'SC']
For meeting lengths above 50, per original meeting, 104 sub-meetings are chosen and modified using the augmentation techniques. For maximum length 200, applying the two best-performing techniques of Sec. The latter technique will be continued for the later stages of the \glscl. Moving to a maximum sub-meeting length of 500 results in an \glsser of 17.73% and for the full sequence length of 20.65%. During finetuning, only \glsdaug is used. Although the \glsser does not always improve, the \glsser on the dev set was reduced in all cases. This is why the finetuned models should be compared with \glssc. For all meeting lengths the \glsdnc model outperforms \glssc. The finetuned \glsdnc model for the full meeting length achieves an \glsser of 16.92%, which reduces \glsser over \glssc by 29.4% relative. The \glsser of a \glsdnc model trained with meeting and \glsdaug, but without \glscl, is only 34.48% after finetuning.