title: From Simulated Mixtures to Simulated Conversations as Training Data for End-to-End Neural Diarization
abstract: End-to-end neural diarization (EEND) is nowadays one of the&#13;\nmost prominent research topics in speaker diarization. EEND&#13;\npresents an attractive alternative to standard cascaded diarization&#13;\nsystems since a single system is trained at once to deal&#13;\nwith the whole diarization problem. Several EEND variants&#13;\nand approaches are being proposed, however, all these models&#13;\nrequire large amounts of annotated data for training but available&#13;\nannotated data are scarce. 
journal: Interspeech 2022