EEND
End-to-End Neural Diarization
**End-to-End Neural Diarization** is a neural network for speaker diarization in which a neural network directly outputs speaker diarization results given a multi-speaker recording. To realize such an end-to-end model, the speaker diarization problem is formulated as a multi-label classification problem and a permutation-free objective function is introduced to directly minimize diarization errors. The EEND method can explicitly handle speaker overlaps during training and inference. Just by feeding multi-speaker recordings with corresponding speaker segment labels, the model can be adapted to real conversations.
End-to-End Neural Diarization: Reformulating Speaker Diarization as Simple Multi-label Classification
https://paperswithcode.com/paper/end-to-end-neural-diarization-reformulating
End-to-End Neural Diarization: Reformulating Speaker Diarization as Simple Multi-label Classification