REVIEW 11 cited by
ASVspoof 2021: accelerating progress in spoofed and deepfake speech detection
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
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 11 Pith papers
-
How Meta-Learning Shapes LoRA Adapter Geometry in Speech Deepfake Detection
Meta-learning training concentrates loss-relevant LoRA updates in query/key projections and spreads them in output projections, relative to standard empirical-risk training.
-
What You Train Is What You Get: Gender Bias, Training Composition, and Post-Hoc Mitigation in Audio Deepfake Detection
Underrepresented gender in training suffers higher deepfake-detection error; WavLM gaps stay large under balance, and all post-hoc calibrations leave the EER gap fixed at 1.317 pp.
-
AUDDT: A Unified Benchmark Toolkit for Audio and Speech Deepfake Detectors
AUDDT packages 28 audio deepfake datasets into a unified benchmarking pipeline and shows that a popular ASVspoof-trained detector's accuracy ranges from 96% to 2% depending on the dataset.
-
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.
-
Improving Generalization for AI-Synthesized Voice Detection
A disentanglement and sharpness-aware training framework improves cross-domain AI-synthesized voice detection by up to 7.59% EER over prior art.
-
Speech-Forensics: Towards Comprehensive Synthetic Speech Dataset Establishment and Analysis
Speech-Forensics combines multi-span partial forgeries with per-span algorithm labels, and the TEST network detects authenticity, localizes fake segments, and identifies synthesis algorithms simultaneously.
-
REIMU: Efficient Heterogeneous Hierarchical Reasoning for SSL-Based Speech Deepfake Detection
A controlled study shows that recurrent and hierarchical backbones do not inherently improve SSL-based speech deepfake detection, while a heterogeneous MHSA-plus-linear-attention design stays competitive with 10.8% fe...
-
Hidden-Domain Routing for All-Type Audio Deepfake Detection
A router-then-specialist audio deepfake detector, which classifies audio type first and then applies type-specific models and thresholds, achieved 96.10% Macro-F1 and first place on AT-ADD Track2.
-
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.
-
Detecting Spoof Voices in Asian Non-Native Speech: An Indonesian and Thai Case Study
Training a fake-voice detector with Indonesian and Thai accented English added to native speech cuts non-native equal error rate from roughly 36-43% to 9-15%.
-
Parallel Stacked Aggregated Network for Voice Authentication in IoT-Enabled Smart Devices
PSA-Net, a light raw-audio network with ResNeXt-style aggregation and squeeze-and-excitation blocks, reports consistent error rates across voice cloning, replay, and chained replay attacks on four benchmarks.
Discussion (0). Continue with ORCID to comment.