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ASVspoof 2021: accelerating progress in spoofed and deepfake speech detection
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ASVspoof 2021 is the forth edition in the series of bi-annual challenges which aim to promote the study of spoofing and the design of countermeasures to protect automatic speaker verification systems from manipulation. In addition to a continued focus upon logical and physical access tasks in which there are a number of advances compared to previous editions, ASVspoof 2021 introduces a new task involving deepfake speech detection. This paper describes all three tasks, the new databases for each of them, the evaluation metrics, four challenge baselines, the evaluation platform and a summary of challenge results. Despite the introduction of channel and compression variability which compound the difficulty, results for the logical access and deepfake tasks are close to those from previous ASVspoof editions. Results for the physical access task show the difficulty in detecting attacks in real, variable physical spaces. With ASVspoof 2021 being the first edition for which participants were not provided with any matched training or development data and with this reflecting real conditions in which the nature of spoofed and deepfake speech can never be predicated with confidence, the results are extremely encouraging and demonstrate the substantial progress made in the field in recent years.
Forward citations
Cited by 5 Pith papers
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SpeechVerifier: Robust Acoustic Fingerprint against Tampering Attacks via Watermarking
SpeechVerifier embeds a contrastively-learned acoustic fingerprint into speech via segment-wise watermarking and detects tampering by comparing the re-derived fingerprint with the embedded one, needing no external reference.
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Hidden-Domain Routing for All-Type Audio Deepfake Detection
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Emoanti: audio anti-deepfake with refined emotion-guided representations
EmoAnti fine-tunes Wav2Vec2 on emotion recognition and refines the resulting features with a convolutional residual extractor, achieving low EER on ASVspoof LA but worse performance on DF than its own no-finetuning baseline.
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