The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
One-Class Knowledge Distillation for Spoofing Speech Detection
Open accessAbstract
The detection of spoofing speech generated by unseen algorithms remains an unresolved challenge. One reason for the lack of generalization ability is that traditional detecting systems follow the binary classification paradigm, which inherently assumes the possession of prior knowledge of spoofing speech. One-class methods attempt to learn the distribution of bonafide speech and are inherently suited to the task where spoofing speech exhibits significant differences. However, training a one-class system using only bonafide speech is challenging. In this paper, we introduce a teacher-student framework to provide guidance for the training of a one-class model. The proposed one-class knowledge distillation method outperforms other state-of-the-art methods on the ASVspoof 21DF and InTheWild datasets, demonstrating its superior generalization ability.
Overview
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
Verification
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.
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?
The cited source says what the claim attributes to it.
In the citing paper
“Common used techniques include meta-learning [20, 182-184], incremental learning [185], knowledge distillation [186], and domain generalization [187].”
From Deepfake detection across image, video, and audio: a comprehensive survey with empirical evaluation of generalization and robustness (2026) · cites this paper as [186]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
ASVspoof 2019: A large-scale public database of synthesized, converted and replayed speech
Wang, X., Yamagishi, J., Todisco, M., Delgado, H., Nautsch, A., Evans, N., Sahidullah, M., Vestman, V., Kinnunen, T., Lee, K. A., et al. (2020) · Computer Speech & Language · doi:10.1016/j.csl.2020.101114 · Cited by 505
Self-citationConstant Q Cepstral Coefficients: A Spoofing Countermeasure for Automatic Speaker Verification
Todisco, M., Delgado, H., & Evans, N. (2017) · Computer Speech & Language · doi:10.1016/j.csl.2017.01.001 · Cited by 424
End-to-End anti-spoofing with RawNet2
Tak, H., Patino, J., Todisco, M., Nautsch, A., Evans, N., & Larcher, A. (2021) · ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp39728.2021.9414234 · Cited by 402
AASIST: Audio Anti-Spoofing Using Integrated Spectro-Temporal Graph Attention Networks
Jung, J.-W., Heo, H.-S., Tak, H., Shim, H.-J., Chung, J. S., Lee, B.-J., Yu, H.-J., & Evans, N. (2022) · ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp43922.2022.9747766 · Cited by 399
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
Does Audio Deepfake Detection Generalize?
Müller, N., Czempin, P., Diekmann, F., Froghyar, A., & Böttinger, K. (2022) · Interspeech 2022 · doi:10.21437/interspeech.2022-108 · Cited by 258
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
Self-citationTak, H., Todisco, M., Wang, X., Jung, J.-W., Yamagishi, J., & Evans, N. (2022) · The Speaker and Language Recognition Workshop (Odyssey 2022) · doi:10.21437/odyssey.2022-16 · Cited by 237
Tak, H., Kamble, M. R., Patino, J., Todisco, M., & Evans, N. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9746213 · Cited by 154
Investigating Self-Supervised Front Ends for Speech Spoofing Countermeasures
Wang, X., & Yamagishi, J. (2022) · The Speaker and Language Recognition Workshop (Odyssey 2022) · doi:10.21437/odyssey.2022-14 · Cited by 138
Research works printed but not linked: 2
- 10.21437/Odyssey.2022-14 · DOI · Paper page
- 10.1109/ICASSP49357.2023.10094779 · DOI
Cited by
Nes2Net: A Lightweight Nested Architecture for Foundation Model Driven Speech Anti-Spoofing
Liu, T., Truong, D.-T., Das, R. K., Lee, K. A., & Li, H. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3626963 · Cited by 36
Mixture of Experts Fusion for Fake Audio Detection Using Frozen wav2vec 2.0
Wang, Z., Fu, R., Wen, Z., Tao, J., Wang, X., Xie, Y., Qi, X., Shi, S., Lu, Y., Liu, Y., et al. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10888990 · Cited by 31
Generalizable Audio Deepfake Detection via Latent Space Refinement and Augmentation
Huang, W. C., Gu, Y., Wang, Z., Zhu, H., & Qian, Y. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10888328 · Cited by 28
A comprehensive survey with critical analysis for deepfake speech detection
Pham, L. D., Lam, P., Tran, D., Tang, H., Nguyen, T., Schindler, A., Skopik, F., Polonsky, A., & Vu, H. C. (2025) · Computer Science Review · doi:10.1016/j.cosrev.2025.100757 · Cited by 28
Wang, B., Tang, Y., Wei, F., Ba, Z., & Ren, K. (2024) · IEEE/ACM Transactions on Audio, Speech, and Language Processing · doi:10.1109/taslp.2024.3492796 · Cited by 20
Advancements in AI-Generated Content Forensics: A Systematic Literature Review
Xu, Q., Mu, W., Li, J., Sun, T., & Jiang, X. (2025) · ACM Computing Surveys · doi:10.1145/3760526 · Cited by 21
Wave-Spectrogram Cross-Modal Aggregation for Audio Deepfake Detection
Jin, Z., Li, L., & Leng, B. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10890563 · Cited by 19
Wang, L., Yu, L., Zhang, Y., & Xie, H. (2024) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2024.3414340 · Cited by 17
Self-citationWhat Does an Audio Deepfake Detector Focus on? A Study in the Time Domain
Grinberg, P., Kumar, A., Koppisetti, S., & Bharaj, G. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10887568 · Cited by 12
Dynamic Ensemble Teacher-Student Distillation Framework for Light-Weight Fake Audio Detection
Xue, J., Fan, C., Yi, J., Zhou, J., & Lv, Z. (2024) · IEEE Signal Processing Letters · doi:10.1109/lsp.2024.3431936 · Cited by 8
A Multi-Stream Fusion Approach with One-Class Learning for Audio-Visual Deepfake Detection
Lee, K., Zhang, Y., & Duan, Z. (2024) · IEEE International Workshop on Multimedia Signal Processing (MMSP) · doi:10.1109/mmsp61759.2024.10743671 · Cited by 7
Self-citationFrequency-mix Knowledge Distillation for Fake Speech Detection
Fan, C., Dong, S., Xue, J., Chen, Y., Yi, J., & Lv, Z. (2024) · Interspeech (USB) · doi:10.21437/interspeech.2024-740 · Cited by 6
Continual Unsupervised Domain Adaptation for Audio Deepfake Detection
Chen, X., Lu, W., Zhang, R., Xu, J., Lu, X., Zhang, L., & Wei, J. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10890538 · Cited by 5
Anomaly Detection and Localization for Speech Deepfakes via Feature Pyramid Matching
Coletta, E., Salvi, D., Negroni, V., Leonzio, D. U., & Bestagini, P. (2025) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco63237.2025.11226156 · Cited by 5
Leveraging distance information for generalized spoofing speech detection
Lu, J., Zhang, Y., Li, Z., Shang, Z., Wang, W., & Zhang, P. (2025) · Computer Speech & Language · doi:10.1016/j.csl.2025.101804 · Cited by 3
Self-citationGeneralized Audio Deepfake Detection Using Frame-level Latent Information Entropy
Zhao, B., Kang, Z., He, Y., Qu, X., Peng, J., Xiao, J., & Wang, J. (2025) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme59968.2025.11210096 · Cited by 3
Hao, Y., Chen, Y., Xu, M., Zhan, J., He, L., Fang, L., Fang, S., & Liu, L. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11228261 · Cited by 3
AFSS: Artifact-Focused Self-Synthesis for Mitigating Bias in Audio Deepfake Detection
Nguyen-Le, H.-S., Nguyen-Thanh, H.-C., Le‐Khac, N., Nguyen, D.-T., & Nguyen-Le, H.-H. (2026) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn69854.2026.11728808 · Cited by 3
Comprehensive Validation of Bridge Module and EBM Loss for One-Class Audio Deepfake Detection
Kim, S.-B., Shin, H.-S., Heo, J., Lim, C.-Y., Koo, K.-W., Son, J., Jung, S., & Yu, H.-J. (2026) · IEEE Access · doi:10.1109/access.2026.3680160 · Cited by 3
Towards Real-Time Audio Deepfake Detection in Resource-Limited Environments
Dinh-Xuan, H., Doan, T.-P., Han, S., Hong, K., & Jung, S. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-7005-5_28 · Cited by 2
Multi-Sinkhorn Teacher Knowledge Aggregation Framework for Adaptive Audio Anti-Spoofing
Zhang, R., Wei, J., Lu, X., Zhang, L., Jin, D., Lu, W., & Xu, J. (2025) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/taslpro.2025.3606191 · Cited by 2
Generalizable Speech Deepfake Detection Via Information Bottleneck Enhanced Adversarial Alignment
Huang, P., Wang, S., Yao, S., & Zhou, M. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11461835 · Cited by 2
Nguyen-Le, H.-H., Tran, V.-T., Nguyen, D.-T., & Le-Khac, N.-A. (2026) · Artificial Intelligence Review · doi:10.1007/s10462-026-11608-4 · Cited by 28
Dynamic knowledge condensation with audio-selective transformer for audio deepfake detection
Wani, T. M., & Amerini, I. (2025) · Discover Computing · doi:10.1007/s10791-025-09746-4 · Cited by 1
Continual Audio Deepfake Detection via Universal Adversarial Perturbation
Li, W., Li, L., & Hong, Q. (2025) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.1109/apsipaasc65261.2025.11249335 · Cited by 1
Moualla, S., Dakkak, O. A., Hamdoun, O., & Jafar, A. (2026) · IEEE Access · doi:10.1109/access.2026.3675783 · Cited by 1
Robust Deepfake Audio Detection via Multi-Level Intermediate Feature Fusion
Zhao, J. S., Zhao, J. S., Zhou, Y., Zheng, P., & Du, Y. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11462275 · Cited by 1
Audio Deepfake Detection: End-to-End training with powerful pretrained ASR
Mansoor, K. H., & Alam, M. (2024) · International Multi-Topic Conference · doi:10.1109/inmic64792.2024.11004412 · Cited by 1
SHDA: Sinkhorn Domain Attention for Cross-Domain Audio Anti-Spoofing
Zhang, R., Wei, J., Lu, X., Zhang, L., Jin, D., Xu, J., & Lu, W. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3576576 · Cited by 1
Bidirectional label distribution-aware margin method for speech spoofing detection
Wang, R., & Fu, X. (2025) · Digital Signal Processing · doi:10.1016/j.dsp.2025.105660
MultiSiFer: Detecting Multiple-Speaker Fake Voice Without Speaker-Irrelative Features
Liu, X., Xuan, H., Yu, Z., Zhang, Z., Fei, Q., & Zhou, Q. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3648834
Wang, H., Fan, C., Zhou, J., Yi, J., & Kwan, H. K. (2026) · Communications in computer and information science · doi:10.1007/978-981-95-5382-2_26
A Robust Region-Aware Framework for Audio Forgery Localization
Luo, J., Wang, H., Liu, H., Hu, S. C., & Wang, K. (2026) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/taslpro.2026.3661237
Robust Speech Forgery Detection via Enhanced Forensic Trace Generation
Wu, S., Cheng, Y., Wang, R., & Yan, D. (2026) · Signal Image and Video Processing · doi:10.1007/s11760-026-05167-y
Cross-Language Audio Spoofing Detection Using GenSpoof-KAN: Tackling English and Arabic Deepfakes
Vijay Ganesh, M., Kavitha, K., Gangasiva, K., Jahnavi, G., Hanusha, K., & Vardhan, N. H. (2026) · International Conference on Communication, Computing and Electronics Systems (ICCCES) · doi:10.1109/iccces62661.2026.11437041
Structured Batching for One-Class Learning in Audio Replay Spoofing Detection
Sánchez-Valera, J. C., Peinado, A. M., Gomez, A. M., & Martín-Doñas, J. M. (2026) · International Workshop on Biometrics and Forensics (IWBF) · doi:10.1109/iwbf68042.2026.11558168
Multi-Prototype Variational Information Bottleneck for SSL-Based Speech Spoofing Detection
Hou, H., Zhou, R., & Yuan, Q. (2026) · IEEE Access · doi:10.1109/access.2026.3722601
HIMLT: A Hybrid Interactive Machine Learning Tool for Whole Slide Image Analysis
Kondreddy, D., Barua, S., Yellu, S., Elghazzali, H., & Lee, S. (2026) · International Conference on Intelligent Multimedia, Networking, and Security (IMNS) · doi:10.1109/imns67862.2026.11655288
Li, W., Pan, H., Li, L., & Hong, Q. (2026) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/taslpro.2026.3739176
Kim, S.-B., Kim, K.-W., & Yu, H.-J. (2026) · IEEE Access · doi:10.1109/access.2026.3742554
Metadata
- Authors
- Lu, J., Zhang, Y., Wang, W., Shang, Z., & Zhang, P.
- Affiliations
- Chinese Academy of Sciences (CN)
- Venue
- ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- Publisher
- IEEE
- Published
- 2024-04-14
- Pages
- 11251-11255
- ISSN
- 1520-6149 (ISSN-L)
- OpenAlex
- W4392903665
- Cited by
- 33
- References
- 21
- Citations by year
- 2026: 9 · 2025: 19 · 2024: 5
- FWCI
- 9.74
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE International Conference on Acoustics Speech and Signal Processing (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Funders
- Research and Development
- 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
- one-class learning, knowledge distillation, teacher-student models, generalization, speaker verification
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
