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In this paper, we propose Discriminative Neural Clustering (DNC) thatformulates data clustering with a maximum number of clusters as a supervisedsequence-to-sequence learning problem. Compared to traditional unsupervisedclustering algorithms, DNC learns clustering patterns from training datawithout requiring an explicit definition of a similarity measure. Animplementation of DNC based on the Transformer architecture is shown to beeffective on a speaker diarisation task using the challenging AMI dataset.Since AMI contains only 147 complete meetings as individual input sequences,data scarcity is a significant issue for training a Transformer model for DNC.Accordingly, this paper proposes three data augmentation schemes: sub-sequencerandomisation, input vector randomisation, and Diaconis augmentation, whichgenerates new data samples by rotating the entire input sequence ofL2-normalised speaker embeddings. Experimental results on AMI show that DNCachieves a reduction in speaker error rate (SER) of 29.4% relative to spectralclustering.

Discriminative Neural Clustering for Speaker Diarisation