ASVspoof 2021: Automatic Speaker Verification Spoofing and Countermeasures Challenge Evaluation Plan
By Héctor Delgado, N. Evans, Tomi H. Kinnunen, Kong Aik LEE, Xuechen Liu, Andreas Nautsch, Jose Patino, Md. Sahidullah, Massimiliano Todisco, Xin Eric Wang and 1 more. 2021
91 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Physiology
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Citations in the corpus
- AudioLM: A Language Modeling Approach to Audio Generation (2022, background): “The ability to continue short speech segments while maintaining speaker identity and prosody can potentially lead to malicious use-cases such as spoofing biometric identification [63] or impersonating a specific speaker [64].”
- Speak, Read and Prompt: High-Fidelity Text-to-Speech with Minimal Supervision (2023, background): “At the same time, we acknowledge that the ability to mimic a voice can have numerous malicious applications, including bypassing biometric identification and for the purpose of imperson-ation (Delgado et al., 2021; Casanova et al., 2022).”