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.
@article{arxiv.2210.16127,
title = {Target-Speaker Voice Activity Detection via Sequence-to-Sequence Prediction},
author = {Ming Cheng and Weiqing Wang and Yucong Zhang and Xiaoyi Qin and Ming Li},
journal= {arXiv preprint arXiv:2210.16127},
year = {2023}
}