# Target-Speaker Voice Activity Detection via Sequence-to-Sequence Prediction

arXiv: https://arxiv.org/abs/2210.16127 · HTML: https://mlsanity.com/paper/2210.16127/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Oct 2022.

## Built on by (most cited first, 6 of 6)

- [The VoxCeleb Speaker Recognition Challenge: A Retrospective](https://mlsanity.com/paper/2408.14886/) (2024)
- [Neural Speaker Diarization Using Memory-Aware Multi-Speaker Embedding with Sequence-to-Sequence Architecture](https://mlsanity.com/paper/2309.09180/) (2023)
- [Comparative Analysis of Personalized Voice Activity Detection Systems: Assessing Real-World Effectiveness](https://mlsanity.com/paper/2406.09443/) (2024)
- [Flow-TSVAD: Target-Speaker Voice Activity Detection via Latent Flow Matching](https://mlsanity.com/paper/2409.04859/) (2024)
- [Speaker Diarization with Overlapping Community Detection Using Graph Attention Networks and Label Propagation Algorithm](https://mlsanity.com/paper/2506.02610/) (2025)
- [Exploring Speaker Diarization with Mixture of Experts](https://mlsanity.com/paper/2506.14750/) (2025)

## Abstract (arXiv)

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.