Towards single integrated spoofing-aware speaker verification embeddings
Open accessAbstract
This study aims to develop a single integrated spoofing-aware speaker verification (SASV) embeddings that satisfy two aspects. First, rejecting non-target speakers' input as well as target speakers' spoofed inputs should be addressed. Second, competitive performance should be demonstrated compared to the fusion of automatic speaker verification (ASV) and countermeasure (CM) embeddings, which outperformed single embedding solutions by a large margin in the SASV2022 challenge. We analyze that the inferior performance of single SASV embeddings comes from insufficient amount of training data and distinct nature of ASV and CM tasks. To this end, we propose a novel framework that includes multi-stage training and a combination of loss functions. Copy synthesis, combined with several vocoders, is also exploited to address the lack of spoofed data. Experimental results show dramatic improvements, achieving a SASV-EER of 1.06% on the evaluation protocol of the SASV2022 challenge.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Still being checked.
Research works printed but not linked: 43
- T. Kinnunen and H. Li, "An overview of text-independent speaker recognition: From features to supervectors," Speech communica- tion, vol. 52, no. 1, 2010.
- Z. Bai and X.-L. Zhang, "Speaker recognition based on deep learning: An overview," Neural Networks, vol. 140, 2021.
- Y. Zhang, Z. Lv, H. Wu et al., "Mfa-conformer: Multi-scale fea- ture aggregation conformer for automatic speaker verification," in Proc. INTERSPEECH, 2022.
- Z. Chen, S. Chen, Y. Wu et al., "Large-scale self-supervised speech representation learning for automatic speaker verification," in Proc. ICASSP, 2022.
- T. Kinnunen, M. Sahidullah, H. Delgado et al., "The ASVspoof 2017 Challenge: Assessing the limits of replay spoofing attack detection," in Proc. INTERSPEECH, 2017.
- X. Wang, J. Yamagishi, M. Todisco et al., "ASVspoof 2019: A large-scale public database of synthesized, converted and replayed speech," Computer Speech & Language, vol. 64, 2020.
- J. Yamagishi, X. Wang, M. Todisco et al., "ASVspoof 2021: ac- celerating progress in spoofed and deepfake speech detection," in Proc. the Automatic Speaker Verification and Spoofing Counter- measures Challenge, 2021.
- J.-w. Jung, H. Tak, H.-j. Shim et al., "SASV 2022: The first spoofing-aware speaker verification challenge," in Proc. INTER- SPEECH, 2022.
- H.-j. Shim, J.-w. Jung, J.-h. Kim and H.-j. Yu, "Integrated replay spoofing-aware text-independent speaker verification," Applied Sciences, vol. 10, no. 18, 2020.
- J. Li, M. Sun, X. Zhang and Y. Wang, "Joint decision of anti- spoofing and automatic speaker verification by multi-task learning with contrastive loss," IEEE Access, vol. 8, 2020.
- M. Todisco, H. Delgado, K. A. Lee et al., "Integrated presenta- tion attack detection and automatic speaker verification: Common features and gaussian back-end fusion," in Proc. INTERSPEECH, 2018.
- Y. Zhang, G. Zhu and Z. Duan, "A probabilistic fusion frame- work for spoofing aware speaker verification," in Proc. Speaker Odyssey, 2022.
- J. Lin, T. Chen, J. Huang et al., "The CLIPS system for 2022 spoofing-aware speaker verification challenge," in Proc. INTER- SPEECH, 2022.
- X. Wang, X. Qin, Y. Wang et al., "The DKU-OPPO system for the 2022 spoofing-aware speaker verification challenge," in Proc. INTERSPEECH, 2022.
- J.-H. Choi, J.-Y. Yang, Y. R. Jeoung and C. J.-H., "HYU sub- mission for the SASV challenge 2022: Reforming speaker em- beddings with spoofing-aware conditioning," in Proc. INTER- SPEECH, 2022.
- W. H. Kang, J. Alam and A. Fathan, "End-to-end framework for spoof-aware speaker verification," in Proc. INTERSPEECH, 2022.
- Z. Teng, Q. Fu, J. White et al., "SA-SASV: An end-to-end spoof- aggregated spoofing-aware speaker verification system," in Proc. INTERSPEECH, 2022.
- W. Ge, H. Tak, M. Todisco and N. Evans, "On the potential of jointly-optimised solutions to spoofing attack detection and auto- matic speaker verification," in Proc. IberSPEECH, 2022.
- A. Alenin, N. Torgashov, A. Okhotnikov et al., "A subnetwork approach for spoofing aware speaker verification," in Proc. IN- TERSPEECH, 2022.
- P. Zhang, P. Hu and X. Zhang, "Norm-constrained score-level ensemble for spoofing aware speaker verification," in Proc. IN- TERSPEECH, 2022.
- L. Zhang, Y. Li, H. Zhao and L. Xie, "Backend ensemble for speaker verification and spoofing countermeasure," in Proc. IN- TERSPEECH, 2022.
- J. S. Chung, A. Nagrani and A. Zisserman, "Voxceleb2: Deep speaker recognition," in Proc. INTERSPEECH, 2018.
- X. Wang and J. Yamagishi, "Spoofed training data for speech spoofing countermeasure can be efficiently created using neural vocoders," in Proc. ICASSP, 2023.
- S. H. Mun, J.-w. Jung, M. H. Han and N. S. Kim, "Frequency and multi-scale selective kernel attention for speaker verification," in Proc. SLT, 2023.
- W. J. Holmes, "Copy synthesis of female speech using the JSRU parallel formant synthesiser.," in Proc. EUROSPEECH, 1989, pp. 2513-2516.
- J. Deng, J. Guo, N. Xue and S. Zafeiriou, "Arcface: Additive an- gular margin loss for deep face recognition," in Proceedings of the IEEE/CVF conference on computer vision and pattern recog- nition, 2019.
- H. S. Heo, B.-J. Lee, J. Huh and J. S. Chung, "Clova baseline sys- tem for the voxceleb speaker recognition challenge 2020," arXiv preprint arXiv:2009.14153, 2020. · arXiv
- Y. Kwon, H. S. Heo, B.-J. Lee and J. S. Chung, "The ins and outs of speaker recognition: lessons from VoxSRC 2020," in Proc. ICASSP, 2021.
- M. Pal, D. Paul and G. Saha, "Synthetic speech detection using fundamental frequency variation and spectral features," Computer Speech & Language, vol. 48, 2018.
- J. Frank and L. Schönherr, "Wavefake: A data set to facilitate au- dio deepfake detection," in Proc. Neural Information Processing Systems Datasets and Benchmarks, 2021.
- Z. Wu, X. Xiao, E. S. Chng and H. Li, "Synthetic speech detection using temporal modulation feature," in Proc. ICASSP, 2013.
- A. Sizov, E. Khoury, T. Kinnunen et al., "Joint speaker verification and antispoofing in the i-vector space," IEEE Transactions on Information Forensics and Security, vol. 10, no. 4, 2015.
- C. Sun, S. Jia, S. Hou et al., "Exposing ai-synthesized hu- man voices using neural vocoder artifacts," arXiv preprint arXiv:2302.09198, 2023. · arXiv
- J. Kong, J. Kim and J. Bae, "HiFi-GAN: Generative adversarial networks for efficient and high fidelity speech synthesis," in Proc. NIPS, 2020, vol. 33.
- R. Prenger, R. Valle and B. Catanzaro, "WaveGlow: A flow-based generative network for speech synthesis," in Proc. ICASSP, 2019.
- X. Wang, S. Takaki and J. Yamagishi, "Neural source-filter-based waveform model for statistical parametric speech synthesis," in Proc. ICASSP, 2019.
- J. Engel, L. Hantrakul, C. Gu and A. Roberts, "DDSP: Differen- tiable digital signal processing," in Proc. ICLR, 2020.
- J. S. Chung, J. Huh, S. Mun et al., "In defence of metric learning for speaker recognition," in Proc. INTERSPEECH, 2020.
- R. Caruana, Multitask learning, Springer, 1998.
- I. Loshchilov and F. Hutter, "Decoupled weight decay regulariza- tion," in Proc. ICLR, 2017.
- B. Desplanques, J. Thienpondt and K. Demuynck, "Ecapa-tdnn: Emphasized channel attention, propagation and aggregation in tdnn based speaker verification," in Proc. INTERSPEECH, 2020.
- J.-w. Jung, H.-S. Heo, H. Tak et al., "Aasist: Audio anti-spoofing using integrated spectro-temporal graph attention networks," in Proc. ICASSP, 2022.
- H.-j. Shim, H. Tak, X. Liu et al., "Baseline systems for the first spoofing-aware speaker verification challenge: score and embed- ding fusion," in Proc. Speaker Odyssey, 2022.
Cited by
Sorted by citations
ASVspoof 5: crowdsourced speech data, deepfakes, and adversarial attacks at scale
Wang, X., Delgado, H., Tak, H., Jung, J.-W., Shim, H.-J., Todisco, M., Kukanov, I., Liu, X., Sahidullah, M., Kinnunen, T. H., et al. (2024) · The Automatic Speaker Verification Spoofing Countermeasures Workshop (ASVspoof 2024) · doi:10.21437/asvspoof.2024-1 · Cited by 101 · see citing works
Self-citationWang, X., & Yamagishi, J. (2024) · ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp48485.2024.10446331 · Cited by 29 · see citing works
Self-citationWang, X., Delgado, H., Tak, H., Jung, J.-W., Shim, H.-J., Todisco, M., Kukanov, I., Liu, X., Sahidullah, M., Kinnunen, T., et al. (2026) · Computer Speech & Language · doi:10.1016/j.csl.2025.101825 · Cited by 30 · see citing works
Self-citationSpoofCeleb: Speech Deepfake Detection and SASV in the Wild
Jung, J.-W., Wu, Y., Wang, X., Kim, J.-H., Maiti, S., Matsunaga, Y., Shim, H.-J., Tian, J., Evans, N., Chung, J. S., et al. (2025) · IEEE Open Journal of Signal Processing · doi:10.1109/ojsp.2025.3529377 · Cited by 21 · see citing works
Self-citationOptimizing a-DCF for Spoofing-Robust Speaker Verification
Kurnaz, O., Mishra, J., Kinnunen, T., & Hanilçi, C. (2025) · IEEE Signal Processing Letters · doi:10.1109/lsp.2025.3545290 · Cited by 2 · see citing works
Self-citationBIM-Based Adversarial Attacks Against Speech Deepfake Detectors
Wang, W. E., Salvi, D., Negroni, V., Leonzio, D. U., Bestagini, P., & Tubaro, S. (2025) · Electronics · doi:10.3390/electronics14152967 · Cited by 5 · see citing works
Assessing the Impact of Speaker Identity in Speech Spoofing Detection
Dao, A.-T., Matrouf, D., & Evans, N. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11462102 · Cited by 1 · see citing works
Self-citationEchoes Unveiled: Identifying Synthetic Voices
Pluth, D., Hosier, J., Zhou, Y., & Gurbani, V. K. (2025) · IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) · doi:10.1109/percomworkshops65533.2025.00134 · Cited by 2 · see citing works
End-to-End Modeling for Speech Spoofing and Deepfake Detection
Tak, H. (2023) · theses.fr (ABES) · doi:10.70675/aa9fb6cbz3f6az45bcz933fzf95d89893e59
Self-citationWu, Y., Jung, J.-W., Shim, H.-J., Cheng, X., & Wang, X. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11461279
Self-citation
- Authors
- Mun, S. H., Shim, H.-J., Tak, H., Wang, X., Liu, X., Sahidullah, M., Jeong, M., Han, M. H., Todisco, M., Lee, K. A., Yamagishi, J., Evans, N., Kinnunen, T., Kim, N. S., & Jung, J.-W.
- ORCID
- Venue
- INTERSPEECH 2023
- Publisher
- ISCA
- Published
- 2023-08-20
- Pages
- 3989-3993
- ISSN
- 2308-457X (ISSN-L)
- OpenAlex
- W4385823275
- Cited by
- 9
- References
- 0
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 1 · 2023: 1
- FWCI
- 1.60
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Interspeech (USB) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Commercializations Promotion Agency for R and D Outcomes
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Physical Sciences › Computer Science › Signal Processing › Biometric Identification and Security
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Keywords
- speaker verification, anti-spoofing, biometric security
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