# The 2022 Far-field Speaker Verification Challenge: Exploring domain mismatch and semi-supervised learning under the far-field scenario

arXiv: https://arxiv.org/abs/2209.05273 · HTML: https://mlsanity.com/paper/2209.05273/

## Key facts

- Cited by 5 papers in the map (data through 19 Oct 2026).
- Placed in Speaker recognition and diarization, one of 11 directions in Speech and audio language models.
- Builds on 10 of its 17 references that are in the map.
- First posted on arXiv 12 Sep 2022.

## Placed in

- Speech and audio language models / [Speaker recognition and diarization](https://mlsanity.com/speech/speaker-recognition-and-diarization/)

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [VoxCeleb2: Deep Speaker Recognition](https://mlsanity.com/paper/1806.05622/) (2018)
- [VoxCeleb: a large-scale speaker identification dataset](https://mlsanity.com/paper/1706.08612/) (2017)
- [MUSAN: A Music, Speech, and Noise Corpus](https://mlsanity.com/paper/1510.08484/) (2015)
- [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://mlsanity.com/paper/2109.06870/) (2021)
- [The DKU-DukeECE Systems for VoxCeleb Speaker Recognition Challenge 2020](https://mlsanity.com/paper/2010.12731/) (2020)
- [The INTERSPEECH 2020 Far-Field Speaker Verification Challenge](https://mlsanity.com/paper/2005.08046/) (2020)
- [The DKU-DukeECE System for the Self-Supervision Speaker Verification Task of the 2021 VoxCeleb Speaker Recognition Challenge](https://mlsanity.com/paper/2109.02853/) (2021)
- [The FFSVC 2020 Evaluation Plan](https://mlsanity.com/paper/2002.00387/) (2020)

## Built on by (most cited first, 5 of 5)

- [VoxBlink: A Large Scale Speaker Verification Dataset on Camera](https://mlsanity.com/paper/2308.07056/) (2023)
- [Overview of Speaker Modeling and Its Applications: From the Lens of Deep Speaker Representation Learning](https://mlsanity.com/paper/2407.15188/) (2024)
- [The DKU-Tencent System for the VoxCeleb Speaker Recognition Challenge 2022](https://mlsanity.com/paper/2210.05092/) (2022)
- [Multi-objective Progressive Clustering for Semi-supervised Domain Adaptation in Speaker Verification](https://mlsanity.com/paper/2310.04760/) (2023)
- [OpenSVBench: Diagnosing Speaker Verification Generalization Across Deployment Scenarios](https://mlsanity.com/paper/2610.13760/) (2026)

## Abstract (arXiv)

FFSVC2022 is the second challenge of far-field speaker verification. FFSVC2022 provides the fully-supervised far-field speaker verification to further explore the far-field scenario and proposes semi-supervised far-field speaker verification. In contrast to FFSVC2020, FFSVC2022 focus on the single-channel scenario. In addition, a supplementary set for the FFSVC2020 dataset is released this year. The supplementary set consists of more recording devices and has the same data distribution as the FFSVC2022 evaluation set. This paper summarizes the FFSVC 2022, including tasks description, trial designing details, a baseline system and a summary of challenge results. The challenge results indicate substantial progress made in the field but also present that there are still difficulties with the far-field scenario.