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ASVspoof 2021: Automatic Speaker Verification Spoofing and Countermeasures Challenge Evaluation Plan

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arxiv 2109.00535 v1 pith:BYBIJPQZ submitted 2021-09-01 eess.AS cs.CRcs.LGcs.SD

ASVspoof 2021: Automatic Speaker Verification Spoofing and Countermeasures Challenge Evaluation Plan

H\'ector Delgado , Nicholas Evans , Tomi Kinnunen , Kong Aik Lee , Xuechen Liu , Andreas Nautsch , Jose Patino , Md Sahidullah
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Massimiliano Todisco Xin Wang Junichi Yamagishi
This is my paper
classification eess.AS cs.CRcs.LGcs.SD
keywords asvspoofcountermeasureschallengeevaluationspoofingautomaticdevelopmentseries
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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The automatic speaker verification spoofing and countermeasures (ASVspoof) challenge series is a community-led initiative which aims to promote the consideration of spoofing and the development of countermeasures. ASVspoof 2021 is the 4th in a series of bi-annual, competitive challenges where the goal is to develop countermeasures capable of discriminating between bona fide and spoofed or deepfake speech. This document provides a technical description of the ASVspoof 2021 challenge, including details of training, development and evaluation data, metrics, baselines, evaluation rules, submission procedures and the schedule.

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Cited by 6 Pith papers

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  4. SONAR: Spectral-Contrastive Audio Residuals for Generalizable Deepfake Detection

    cs.SD 2025-11 conditional novelty 6.0

    SONAR improves audio deepfake detection by explicitly aligning low- and high-frequency representations for real speech and repelling them for fakes, setting new benchmark EERs on ASVspoof 2021 and in-the-wild data.

  5. Deepfake Audio Detection Using Self-supervised Fusion Representations

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