  Target-speaker voice activity detection is currently a promising approach for
speaker diarization in complex acoustic environments. This paper presents a
novel Sequence-to-Sequence Target-Speaker Voice Activity Detection
(Seq2Seq-TSVAD) method that can efficiently address the joint modeling of
large-scale speakers and predict high-resolution voice activities. Experimental
results show that larger speaker capacity and higher output resolution can
significantly reduce the diarization error rate (DER), which achieves the new
state-of-the-art performance of 4.55% on the VoxConverse test set and 10.77% on
Track 1 of the DIHARD-III evaluation set under the widely-used evaluation
metrics.
