EEND
End-to-End Neural Diarization (EEND) is an architecture that formulates speaker diarization as a multi-label classification task. It natively handles overlapping speech and represents one of the mainstream technical approaches in the current speaker diarization field.
Timeline (last 90 days)
The EEND (End-to-End Neural Diarization) architecture models the diarization problem as a multi-label classification task, natively supporting overlapping speech.
The EDA (Encoder-Decoder based Attractor) mechanism extends upon EEND and is capable of handling scenarios where the number of speakers changes dynamically.
All Facts (2)
The EEND (End-to-End Neural Diarization) architecture models the diarization problem as a multi-label classification task, natively supporting overlapping speech.
50%UnverifiedThe EDA (Encoder-Decoder based Attractor) mechanism extends upon EEND and is capable of handling scenarios where the number of speakers changes dynamically.
50%