Comparative Analysis of XAI Approaches for Synthetic Speech Detection
DOI:
https://doi.org/10.71310/pcam.5_76.2026.06Keywords:
explainable artificial intelligence, XAI, synthetic speech detection, audio deepfake detection, interpretabilityAbstract
To date, speech synthesis systems have advanced to the point where the automatic detection of synthetic and deepfake speech has become increasingly challenging. At the same time, most detection models remain largely opaque “black boxes”, which limits their applicability in digital forensics, biometrics, and other critical systems. This study reviews and comparatively analyzes explainable artificial intelligence (XAI) methods applied specifically to synthetic and deepfake speech detection. Gradient-based, relevance-based, and model-agnostic approaches are examined, including Integrated Gradients, Deep Taylor Decomposition, Layer-wise Relevance Propagation, LIME, SHAP, and Grad-CAM. Particular attention is paid to the principles underlying explanation generation and the interpretability of explanations in relation to the acoustic characteristics of speech signals. Approaches to evaluating explanation quality are discussed, including faithfulness, stability, and sensitivity to perturbations, and the limitations of published studies are analyzed. The comparison provides a structured overview of the strengths and limitations of different XAI methods, identifies challenges in comparing existing results, and outlines promising directions for future research.
References
Zhang B., Cui H., Nguyen V. [et al.] Audio deepfake detection: what has been achieved and what lies ahead // Sensors. — 2025. — Vol. 25, №7. — Art. 1989. — doi: https://doi.org/10.3390/s25071989.
Mai K. T., Bray S., Davies T. [et al.] Warning: humans cannot reliably detect speech deepfakes // PLoS ONE. — 2023. — Vol. 18, №8. — Art. e0285333. — doi: https://doi.org/10.1371/journal.pone.0285333.
Warren K., Tucker T., Crowder A. [et al.] «Better be computer or I’m dumb»: a large-scale evaluation of humans as audio deepfake detectors // Proceedings of the 2024 ACM SIGSAC Conference on Computer and Communications Security (CCS ’24). — 2024. — New York: ACM. — P. 2696–2710. — doi: https://doi.org/10.1145/3658644.3670325.
Wang X., Delgado H., Evans N. [et al.] ASVspoof 5: evaluation of spoofing, deepfake, and adversarial attack detection using crowdsourced speech // IEEE Transactions on Audio, Speech and Language Processing. — 2026. — Vol. 34. — P. 2354–2367. — doi: https://doi.org/10.1109/TASLPRO.2026.3682962.
Momin M. S., Sufian A., Barman D. [et al.] Explainable deepfake detection across different modalities: an overview of methods and challenges // Image and Vision Computing. — 2025. — Vol. 163. — Art. 105738. — doi: https://doi.org/10.1016/j.imavis.2025.105738.
Bisogni C., Loia V., Nappi M. [et al.] Acoustic features analysis for explainable machine learning-based audio spoofing detection // Computer Vision and Image Understanding. — 2024. — Vol. 249. — Art. 104145. — doi: https://doi.org/10.1016/j.cviu.2024.104145.
Schwalbe G., Finzel B. A comprehensive taxonomy for explainable artificial intelligence: a systematic survey of surveys on methods and concepts // Data Mining and Knowledge Discovery. — 2024. — Vol. 38. — P. 3043–3101. — doi: https://doi.org/10.1007/s10618-022-00867-8.
Yang W., Wei Y., Wei H. [et al.] Survey on explainable AI: from approaches, limitations and applications aspects // Human-Centric Intelligent Systems. — 2023. — Vol. 3. — P. 161–188. — doi: https://doi.org/10.1007/s44230-023-00038-y.
Budhkar A., Song Q., Su J., Zhang X. Demystifying the black box: a survey on explainable artificial intelligence (XAI) in bioinformatics // Computational and Structural Biotechnology Journal. — 2025. — Vol. 27. — P. 346–359. — doi: https://doi.org/10.1016/j.csbj.2024.12.011.
Salih A. O. M., Emam H. M., Ahmed A. B. G. E. [et al.] Deepfake audio detection in voice authentication: a spectral and CNN-based comprehensive review // Engineering, Technology & Applied Science Research. — 2025. — Vol. 15, №6. — P. 29824–29832. — doi: https://doi.org/10.48084/etasr.13400.
Almutairi Z., Elgibreen H. A review of modern audio deepfake detection methods: challenges and future directions // Algorithms. — 2022. — Vol. 15, №5. — Art. 155. — doi: https://doi.org/10.3390/a15050155.
Tahaoglu G., Baracchi D., Shullani D. [et al.] Deepfake audio detection with spectral features and ResNeXt-based architecture // Knowledge-Based Systems. — 2025. — Vol. 323. — Art. 113726. — doi: https://doi.org/10.1016/j.knosys.2025.113726.
Asuai C., Arinomor A. P., Atumah C. T. [et al.] Hybrid CNN-LSTM architectures for deepfake audio detection using Mel frequency cepstral coefficients and spectrogram analysis // American Journal of Mathematical and Computer Modelling. — 2025. — Vol. 10, №3. — P. 98–109. — doi: https://doi.org/10.11648/j.ajmcm.20251003.12.
Firc A., Malinka K., Hanacek P. Evaluation framework for deepfake speech detection: a comparative study of state-of-the-art deepfake speech detectors // Cybersecurity. — 2025. — Vol. 8. — Art. 1. — doi: https://doi.org/10.1186/s42400-024-00346-1.
Mersha M., Lam K., Wood J. [et al.] Explainable artificial intelligence: a survey of needs, techniques, applications, and future direction // Neurocomputing. — 2024. — Vol. 599. — Art. 128111. — doi: https://doi.org/10.1016/j.neucom.2024.128111.
Spinner T., Schlegel U., Sch¨afer H. [et al.] explAIner: a visual analytics framework for interactive and explainable machine learning // IEEE Transactions on Visualization and Computer Graphics. — 2020. — Vol. 26, №1. — P. 1064–1074. — doi: https://doi.org/10.1109/TVCG.2019.2934629.
Naresh B., Ch R., Reddy K. S. [et al.] Enhancing the detection of deepfake audio using explainable AI techniques for secure and trustworthy applications // 2025 International Conference on Applications of Machine Intelligence and Data Analytics (ICAMIDA). — Aurangabad, 2025. — P. 1–6. — doi: https://doi.org/10.1109/ICAMIDA64673.2025.11209741.
Sundararajan M., Taly A., Yan Q. Axiomatic attribution for deep networks // Proceedings of the 34th International Conference on Machine Learning (PMLR). — 2017. — Vol. 70. — P. 3319–3328.
Cheng Z., Wu Y., Li Y. [et al.] A comprehensive review of explainable artificial intelligence (XAI) in computer vision // Sensors. — 2025. — Vol. 25, №13. — Art. 4166. — doi: https://doi.org/10.3390/s25134166.
Lim S.-Y., Chae D.-K., Lee S.-C. Detecting deepfake voice using explainable deep learning techniques // Applied Sciences. — 2022. — Vol. 12, №8. — Art. 3926. — doi: https://doi.org/10.3390/app12083926.
Govindu A., Kale P., Hullur A. [et al.] Deepfake audio detection and justification with explainable artificial intelligence (XAI). — Preprint (Version 1). — Research Square, 2023. — doi: https://doi.org/10.21203/rs.3.rs-3444277/v1.
Holzinger A., Saranti A., Molnar C. [et al.] Explainable AI methods — a brief overview // xxAI — Beyond Explainable AI. Lecture Notes in Computer Science. — 2022. — Vol. 13200. — P. 13–38. — doi: https://doi.org/10.1007/978-3-031-04083-2_2.
Ge W., Patino J., Todisco M. [et al.] Explaining deep learning models for spoofing and deepfake detection with SHapley Additive exPlanations // 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). — 2022. — P. 6387–6391. — doi: https://doi.org/10.1109/ICASSP43922.2022.9747476.
Kwak I.-Y., Kwak S., Lee J. [et al.] Voice spoofing detection through residual network, max feature map, and depthwise separable convolution // IEEE Access. — 2023. — Vol. 11. — P. 49140–49152. — doi: https://doi.org/10.1109/ACCESS.2023.3275790.
Akman A., Schuller B. W. Audio explainable artificial intelligence: a review // Intelligent Computing. — 2024. — Vol. 3. — Art. 0074. — doi: https://doi.org/10.34133/icomputing.0074.
Grinberg P., Kumar A., Koppisetti S. [et al.] What does an audio deepfake detector focus on? A study in the time domain // ICASSP 2025 — 2025 IEEE International Conference on Acoustics, Speech and Signal Processing. — 2025. — Hyderabad. — P. 1–5. — doi: https://doi.org/10.1109/ICASSP49660.2025.10887568.
Cheng Z., Yang H., Xiong Y. [et al.] Explainable AI for forensic speech authentication within cognitive and computational neuroscience // Frontiers in Neuroscience. — 2025. — Vol. 19. — Art. 1692122. — doi: https://doi.org/10.3389/fnins.2025.1692122.
Baniecki H., Biecek P. Adversarial attacks and defenses in explainable artificial intelligence: a survey // Information Fusion. — 2024. — Vol. 107. — Art. 102303. — doi: https://doi.org/10.1016/j.inffus.2024.102303.
Alvarez-Melis D., Jaakkola T. S. Towards robust interpretability with self-explaining neural networks // Advances in Neural Information Processing Systems (NeurIPS 2018). — 2018. — Vol. 31. — P. 7786–7795.
Ridley M. Human-centered explainable artificial intelligence: an Annual Review of Information Science and Technology (ARIST) paper // Journal of the Association for Information Science and Technology. — 2025. — Vol. 76, №1. — P. 98–120. — doi: https://doi.org/10.1002/asi.24889.
Kim J., Maathuis H., Sent D. Human-centered evaluation of explainable AI applications: a systematic review // Frontiers in Artificial Intelligence. — 2024. — Vol. 7. — Art. 1456486. — doi: https://doi.org/10.3389/frai.2024.1456486.
Yang T., Sun C., Lyu S. [et al.] Forensic deepfake audio detection using segmental speech features // Forensic Science International. — 2026. — Vol. 379. — Art. 112768. — doi: https://doi.org/10.1016/j.forsciint.2025.112768.
Pad´ın-Torrente H., Carneiro-Diaz V., Ortega-Fernandez I. Toward human-centered explainability: natural language explanations for anomaly detection // Information Systems Frontiers. — 2026. — doi: https://doi.org/10.1007/s10796-026-10717-3.
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