A study on data augmentation in voice anti-spoofing
Open accessIntegrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
E2E architectures and SSL-based frontends have led to substantial improvements in performance for the LA task, even for single systems (not ensembles), with best t-DCF being 0.2066 (best EER 0.82%) for the ASVspoof 2021 LA database.
“While conventional acoustic features remain popular [45], E2E architectures and SSL-based frontends have led to substantial improvements in performance for the LA task, even for single systems (not ensembles), with best t-DCF being 0.2066 (best EER 0.82%) for the ASVspoof 2021 LA database [42].”
From ASVspoof 2021: Towards Spoofed and Deepfake Speech Detection in the Wild (2023) · cites this paper as [45]
Data augmentation techniques substantially improve the robustness of spoofed audio detection.
“All these studies indicated that the robustness of spoofed audio detection is substantially improved when DA techniques are employed.”
From ASVspoof 2021: Towards Spoofed and Deepfake Speech Detection in the Wild (2023) · cites this paper as [45]
Existing Audio Deepfake Detection (ADD) systems often struggle to generalise effectively due to significantly degraded audio quality caused by audio codec compression and channel transmission effects in real-world communication scenarios.
“Existing Audio Deepfake Detection (ADD) systems often struggle to generalise effectively due to the significantly degraded audio quality caused by audio codec compression and channel transmission effects in real-world communication scenarios.”
From Benchmarking Audio Deepfake Detection Robustness in Real-World Communication Scenarios (2025) · cites this paper as [5]
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
Decoupled Weight Decay Regularization
Loshchilov, I., & Hutter, F. (2017) · arXiv · doi:10.48550/arxiv.1711.05101 · Cited by 12,522 · see citing works
SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition
Park, D. S., Chan, W., Zhang, Y., Chiu, C.-C., Zoph, B., Cubuk, E. D., & Le, Q. V. (2019) · Interspeech 2019 · doi:10.21437/interspeech.2019-2680 · Cited by 3,558 · see citing works
Understanding the Effective Receptive Field in Deep Convolutional Neural Networks
Luo, W., Li, Y., Urtasun, R., & Zemel, R. (2017) · arXiv · doi:10.48550/arxiv.1701.04128 · Cited by 1,575 · see citing works
2020 International Joint Conference on Neural Networks (IJCNN)
(2020) · doi:10.1109/ijcnn48605.2020 · Cited by 786 · see citing works
ASVspoof 2019: Future Horizons in Spoofed and Fake Audio Detection
Todisco, M., Wang, X., Vestman, V., Sahidullah, M., Delgado, H., Nautsch, A., Yamagishi, J., Evans, N., Kinnunen, T. H., & Lee, K. A. (2019) · Interspeech 2019 · doi:10.21437/interspeech.2019-2249 · Cited by 687 · see citing works
An Introduction to Text-to-Speech Synthesis
Dutoit, T. (1997) · Text, speech and language technology · doi:10.1007/978-94-011-5730-8 · Cited by 633 · see citing works
The ASVspoof 2017 challenge: assessing the limits of replay spoofing attack detection
Kinnunen, T., Sahidullah, M., Delgado, H., Todisco, M., Evans, N., Yamagishi, J., & Lee, K. A. (2017) · Interspeech 2017 · doi:10.21437/interspeech.2017-1111 · Cited by 535 · see citing works
ASVspoof 2015: the first automatic speaker verification spoofing and countermeasures challenge
Wu, Z., Kinnunen, T., Evans, N., Yamagishi, J., Hanilçi, C., Sahidullah, M., & Sizov, A. (2015) · Interspeech 2015 · doi:10.21437/interspeech.2015-462 · Cited by 484 · see citing works
ASVspoof 2021: accelerating progress in spoofed and deepfake speech detection
Yamagishi, J., Wang, X., Todisco, M., Sahidullah, M., Patino, J., Nautsch, A., Liu, X., Lee, K. A., Kinnunen, T., Evans, N., et al. (2021) · 2021 Edition of the Automatic Speaker Verification and Spoofing Countermeasures Challenge · doi:10.21437/asvspoof.2021-8 · Cited by 380 · see citing works
ISO/IEC MPEG-2 Advanced Audio Coding
Bosi, M., Brandenburg, K., Quackenbush, S., et al. (1997) · Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft) · Cited by 314 · see citing works
Converting video formats with FFmpeg
Tomar, S. (2006) · Linux journal · Cited by 278 · see citing works
STC Antispoofing Systems for the ASVspoof2019 Challenge
Lavrentyeva, G., Novoselov, S., Andzhukaev, T., Volkova, M., Gorlanov, A., & Kozlov, A. (2019) · Interspeech (USB) · doi:10.21437/interspeech.2019-1768 · Cited by 256 · see citing works
One-class learning towards synthetic voice spoofing detection
Zhang, Y., Jiang, F., & Duan, Z. (2021) · IEEE Signal Processing Letters · doi:10.1109/lsp.2021.3076358 · Cited by 241 · see citing works
(2013) · Choice Reviews Online · doi:10.5860/choice.50-3181 · Cited by 205 · see citing works
Generalization of Audio Deepfake Detection
Chen, T., Kumar, A., Nagarsheth, P., Sivaraman, G., & Khoury, E. (2020) · The Speaker and Language Recognition Workshop (Odyssey 2020) · doi:10.21437/odyssey.2020-19 · Cited by 200 · see citing works
Towards End-to-End Synthetic Speech Detection
Hua, G., Teoh, A. B. J., & Zhang, H. (2021) · IEEE Signal Processing Letters · doi:10.1109/lsp.2021.3089437 · Cited by 186 · see citing works
ASSERT: Anti-Spoofing with Squeeze-Excitation and Residual Networks
Lai, C.-I., Chen, N., Villalba, J., & Dehak, N. (2019) · Interspeech (USB) · doi:10.21437/interspeech.2019-1794 · Cited by 161 · see citing works
A Comparative Study on Recent Neural Spoofing Countermeasures for Synthetic Speech Detection
Wang, X., & Yamagishi, J. (2021) · Interspeech 2021 · doi:10.21437/interspeech.2021-702 · Cited by 137 · see citing works
ASVspoof 2021: Automatic Speaker Verification Spoofing and Countermeasures Challenge Evaluation Plan
Delgado, H., Evans, N., Kinnunen, T., Lee, K. A., Liu, X., Nautsch, A., Patino, J., Sahidullah, M., Todisco, M., Wang, X., et al. (2021) · arXiv · doi:10.48550/arxiv.2109.00535 · Cited by 129 · see citing works
Voice Conversion Challenge 2020 –- Intra-lingual semi-parallel and cross-lingual voice conversion –-
Yi, Z., Huang, W.-C., Tian, X., Yamagishi, J., Das, R. K., Kinnunen, T., Ling, Z.-H., & Toda, T. (2020) · Joint Workshop for the Blizzard Challenge and Voice Conversion Challenge 2020 · doi:10.21437/vcc_bc.2020-14 · Cited by 126 · see citing works
Tandem Assessment of Spoofing Countermeasures and Automatic Speaker Verification: Fundamentals
Kinnunen, T., Delgado, H., Evans, N., Lee, K. A., Vestman, V., Nautsch, A., Todisco, M., Wang, X., Sahidullah, M., Yamagishi, J., et al. (2020) · IEEE/ACM Transactions on Audio, Speech, and Language Processing · doi:10.1109/taslp.2020.3009494 · Cited by 108 · see citing works
A Capsule Network Based Approach for Detection of Audio Spoofing Attacks
Luo, A., Li, E., Liu, Y., Kang, X., & Wang, Z. J. (2021) · ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp39728.2021.9414670 · Cited by 84 · see citing works
Graph Attention Networks for Anti-Spoofing
Tak, H., Jung, J.-W., Patino, J., Todisco, M., & Evans, N. (2021) · Interspeech 2021 · doi:10.21437/interspeech.2021-993 · Cited by 79 · see citing works
Spectral Features for Synthetic Speech Detection
Paul, D., Pal, M., & Saha, G. (2017) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2017.2684705 · Cited by 77 · see citing works
Channel-Wise Gated Res2Net: Towards Robust Detection of Synthetic Speech Attacks
Li, X., Wu, X., Lu, H., Liu, X., & Meng, H. M. L. (2021) · Interspeech (USB) · doi:10.21437/interspeech.2021-2125 · Cited by 76 · see citing works
Spoofing Attack Detection Using the Non-Linear Fusion of Sub-Band Classifiers
Tak, H., Patiño, J. F., Nautsch, A., Evans, N., & Todisco, M. (2020) · Interspeech (USB) · doi:10.21437/interspeech.2020-1844 · Cited by 69 · see citing works
The Voice Conversion Challenge 2018: Promoting Development of Parallel and Nonparallel MethodsPreprint
Lorenzo-Trueba, J., Yamagishi, J., Toda, T., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1804.04262 · Cited by 66 · see citing works
UR Channel-Robust Synthetic Speech Detection System for ASVspoof 2021
Chen, X., Zhang, Y., Zhu, G., & Duan, Z. (2021) · 2021 Edition of the Automatic Speaker Verification and Spoofing Countermeasures Challenge · doi:10.21437/asvspoof.2021-12 · Cited by 61 · see citing works
G.722: a new CCITT coding standard for digital transmission of wideband audio signals
Mermelstein, P. G. (1988) · IEEE Communications Magazine · doi:10.1109/35.417 · Cited by 59 · see citing works
Modified Magnitude-Phase Spectrum Information for Spoofing Detection
Yang, J., Wang, H., Das, R. K., & Qian, Y. (2021) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2021.3060810 · Cited by 53 · see citing works
Tak, H., Patino, J., Nautsch, A., Evans, N., & Todisco, M. (2020) · The Speaker and Language Recognition Workshop (Odyssey) · doi:10.21437/odyssey.2020-47 · Cited by 38 · see citing works
Pindrop Labs' Submission to the ASVspoof 2021 Challenge
Chen, T., Khoury, E., Phatak, K., & Sivaraman, G. (2021) · 2021 Edition of the Automatic Speaker Verification and Spoofing Countermeasures Challenge · doi:10.21437/asvspoof.2021-14 · Cited by 30 · see citing works
The Impact of Audio Input Representations on Neural Network based Music Transcription
Cheuk, K. W., Agres, K. R., & Herremans, D. (2020) · doi:10.1109/ijcnn48605.2020.9207605 · Cited by 21 · see citing works
ASSERT: Anti-Spoofing with Squeeze-Excitation and Residual neTworksPreprint
Lai, C.-I., Chen, N., Villalba, J., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.01120 · Cited by 18 · see citing works
Overview of compression and packet loss effects in speech biometrics
Besacier, L., Mayorga, P., Bonastre, J.-F., Fredouille, C., & Meignier, S. (2003) · IEE Proceedings - Vision, Image, and Signal Processing · doi:10.1049/ip-vis:20031033 · Cited by 17 · see citing works
Kobayashi, K., Huang, W.-C., Wu, Y.-C., Tobing, P. L., Hayashi, T., & Toda, T. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9413959 · Cited by 16 · see citing works
Automatic speaker verification on narrowband and wideband lossy coded clean speech
Jarina, R., Polacký, J., Počta, P., et al. (2017) · IET Biometrics · doi:10.1049/iet-bmt.2016.0119 · Cited by 15 · see citing works
Speaker recognition on lossy compressed speech using the speex codec
Stauffer, A., & Lawson, A. D. (2009) · Interspeech (USB) · doi:10.21437/interspeech.2009-399 · Cited by 12 · see citing works
Tak, H., Patino, J., Nautsch, A., Evans, N., & Todisco, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.06422 · Cited by 4 · see citing works
Dynamically Mitigating Data Discrepancy with Balanced Focal Loss for Replay Attack Detection
Dou, Y., Yang, H., Yang, M., Xu, Y., & Ke, D. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9412749 · Cited by 4 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
TIMIT-TTS: A Text-to-Speech Dataset for Multimodal Synthetic Media Detection
Salvi, D., Hosler, B., Bestagini, P., Stamm, M. C., & Tubaro, S. (2023) · IEEE Access · doi:10.1109/access.2023.3276480 · Cited by 65 · see citing works
Domain Generalization via Aggregation and Separation for Audio Deepfake Detection
Xie, Y., Cheng, H., Wang, Y., & Ye, L. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2023.3324724 · Cited by 59 · see citing works
Towards Attention-based Contrastive Learning for Audio Spoof Detection
Goel, C., Koppisetti, S., Colman, B., Shahriyari, A., & Bharaj, G. (2023) · INTERSPEECH 2023 · doi:10.21437/interspeech.2023-245 · Cited by 7 · see citing works
Benchmarking Audio Deepfake Detection Robustness in Real-World Communication Scenarios
Shi, H., Shi, X., Dogan, S., Alzubi, S., Huang, T., & Zhang, Y. (2025) · 2025 33rd European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco63237.2025.11226601 · Cited by 8 · see citing works
“GS1016) and the China Scholarship Council (Grant No. 202208060237). channel transmission effects degrade audio quality [5].”
ASVspoof 2021: Towards Spoofed and Deepfake Speech Detection in the Wild
Liu, X., Wang, X., Sahidullah, M., Patino, J., Delgado, H., Kinnunen, T., Todisco, M., Yamagishi, J., Evans, N., Nautsch, A., et al. (2023) · IEEE/ACM Transactions on Audio, Speech, and Language Processing · doi:10.1109/taslp.2023.3285283 · Cited by 312 · see citing works
- Authors
- Cohen, A., Rimon, I., Aflalo, E., & Permuter, H. H.
- Affiliations
- Ben-Gurion University of the Negev (IL)
- Corresponding author
- Cohen, Ariel
- Venue
- Speech Communication
- Publisher
- Elsevier BV
- Published
- 2022-06
- Volume
- 141
- Pages
- 56-67
- ISSN
- 0167-6393 (ISSN-L)
- OpenAlex
- W3207579445
- Cited by
- 60
- References
- 47
- Citations by year
- 2026: 8 · 2025: 19 · 2024: 17 · 2023: 12 · 2022: 4
- FWCI
- 4.28
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Speech Communication (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Physical Sciences › Computer Science › Signal Processing › Biometric Identification and Security
- Keywords
- data augmentation, channel variation, audio compression, spoofing attacks, online data augmentation, voice biometrics, ASVspoof 2021
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review