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

TL;DR

The FFSVC2022 challenge advances far-field speaker verification by exploring domain mismatch and semi-supervised learning, highlighting progress and ongoing challenges in single-channel scenarios.

Contribution

This paper introduces the second FFSVC challenge focusing on fully-supervised and semi-supervised far-field speaker verification in single-channel scenarios, with new datasets and baseline systems.

Findings

Significant progress in far-field speaker verification

Persistent challenges with domain mismatch in far-field scenarios

Baseline systems provide a reference for future research

Abstract

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.