{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T08:44:02Z","timestamp":1767084242567,"version":"3.44.0"},"reference-count":62,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2025,4,21]],"date-time":"2025-04-21T00:00:00Z","timestamp":1745193600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,4,21]],"date-time":"2025-04-21T00:00:00Z","timestamp":1745193600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2025,5]]},"DOI":"10.1007\/s10489-025-06272-0","type":"journal-article","created":{"date-parts":[[2025,4,21]],"date-time":"2025-04-21T03:46:47Z","timestamp":1745207207000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Understanding and leveraging vocoder fingerprints for synthetic speech attribution"],"prefix":"10.1007","volume":"55","author":[{"given":"Jianpeng","family":"Ke","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lina","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,4,21]]},"reference":[{"key":"6272_CR1","unstructured":"Ren Y, Hu C, Tan X, Qin T, Zhao S, Zhao Z, Liu T (2021) Fastspeech 2: Fast and high-quality end-to-end text to speech. In: 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021. OpenReview.net. https:\/\/openreview.net\/forum?id=piLPYqxtWuA"},{"key":"6272_CR2","doi-asserted-by":"publisher","unstructured":"Shen J, Pang R, Weiss RJ, Schuster M, Jaitly N, Yang Z, Chen Z, Zhang Y, Wang Y, Ryan R, Saurous RA, Agiomyrgiannakis Y, Wu Y (2018) Natural TTS synthesis by conditioning wavenet on MEL spectrogram predictions. In: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2018, Calgary, AB, Canada, April 15-20, 2018, IEEE, pp 4779\u20134783. https:\/\/doi.org\/10.1109\/ICASSP.2018.8461368","DOI":"10.1109\/ICASSP.2018.8461368"},{"key":"6272_CR3","doi-asserted-by":"publisher","unstructured":"Wang Y, Skerry-Ryan RJ, Stanton D, Wu Y, Weiss RJ, Jaitly N, Yang Z, Xiao Y, Chen Z, Bengio S, Le QV, Agiomyrgiannakis Y, Clark R, Saurous RA (2017) Tacotron: Towards end-to-end speech synthesis. In: Lacerda F (ed) Interspeech 2017, 18th Annual Conference of the International Speech Communication Association, Stockholm, Sweden, August 20-24, 2017, ISCA, pp 4006\u20134010.https:\/\/doi.org\/10.21437\/INTERSPEECH.2017-1452","DOI":"10.21437\/INTERSPEECH.2017-1452"},{"issue":"6","key":"6272_CR4","doi-asserted-by":"publisher","first-page":"4507","DOI":"10.1007\/S10489-024-05380-7","volume":"54","author":"S Ghosh","year":"2024","unstructured":"Ghosh S, Sarkar S, Ghosh S, Zalkow F, Jana ND (2024) Audio-visual speech synthesis using vision transformer-enhanced autoencoders with ensemble of loss functions. Appl Intell 54(6):4507\u20134524. https:\/\/doi.org\/10.1007\/S10489-024-05380-7","journal-title":"Appl Intell"},{"key":"6272_CR5","doi-asserted-by":"publisher","first-page":"1324","DOI":"10.1109\/TASLP.2024.3359352","volume":"32","author":"M Baas","year":"2024","unstructured":"Baas M, Kamper H (2024) Disentanglement in a GAN for unconditional speech synthesis. IEEE ACM Trans Audio Speech Lang Process 32:1324\u20131335. https:\/\/doi.org\/10.1109\/TASLP.2024.3359352","journal-title":"IEEE ACM Trans Audio Speech Lang Process"},{"key":"6272_CR6","doi-asserted-by":"crossref","unstructured":"Valle R, Li J, Prenger R, Catanzaro B (2020) Mellotron: multispeaker expressive voice synthesis by conditioning on rhythm, pitch and global style tokens. In: ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 6189\u20136193","DOI":"10.1109\/ICASSP40776.2020.9054556"},{"key":"6272_CR7","doi-asserted-by":"publisher","unstructured":"Casanova E, Shulby C, G\u00f6lge E, M\u00fcller NM, Oliveira FS Jr, A.C., Silva Soares, A., Alu\u00edsio, S.M., Ponti, M.A. (2021) Sc-glowtts: An efficient zero-shot multi-speaker text-to-speech model. In: Hermansky H, Cernock\u00fd H, Burget L, Lamel L, Scharenborg O, Motl\u00edcek P (eds) Interspeech 2021, 22nd Annual Conference of the International Speech Communication Association, Brno, Czechia, 30 August - 3 September 2021, ISCA, pp 3645\u20133649. https:\/\/doi.org\/10.21437\/INTERSPEECH.2021-1774","DOI":"10.21437\/INTERSPEECH.2021-1774"},{"key":"6272_CR8","doi-asserted-by":"publisher","unstructured":"Kaneko T, Kameoka H, Tanaka K, Hojo N (2019) Stargan-vc2: Rethinking conditional methods for stargan-based voice conversion. In: Kubin G, Kacic Z (eds) Interspeech 2019, 20th Annual Conference of the International Speech Communication Association, Graz, Austria, 15-19 September 2019, ISCA, pp 679\u2013683. https:\/\/doi.org\/10.21437\/INTERSPEECH.2019-2236","DOI":"10.21437\/INTERSPEECH.2019-2236"},{"key":"6272_CR9","doi-asserted-by":"publisher","first-page":"101114","DOI":"10.1016\/J.CSL.2020.101114","volume":"64","author":"X Wang","year":"2020","unstructured":"Wang X, Yamagishi J, Todisco M, Delgado H, Nautsch A, Evans NWD, Sahidullah M, Vestman V, Kinnunen T, Lee KA, Juvela L, Alku P, Peng Y, Hwang H, Tsao Y, Wang H, Maguer SL, Becker M, Ling Z (2020) Asvspoof 2019: a large-scale public database of synthesized, converted and replayed speech. Comput Speech Lang 64:101114. https:\/\/doi.org\/10.1016\/J.CSL.2020.101114","journal-title":"Comput Speech Lang"},{"key":"6272_CR10","doi-asserted-by":"crossref","unstructured":"Yamagishi J, Wang X, Todisco M, Sahidullah M, Patino J, Nautsch A, Liu X, Lee KA, Kinnunen T, Evans N et al (2021) Asvspoof 2021: accelerating progress in spoofed and deepfake speech detection. arXiv:2109.00537","DOI":"10.21437\/ASVSPOOF.2021-8"},{"key":"6272_CR11","doi-asserted-by":"publisher","unstructured":"Jung J, Heo H, Tak H, Shim H, Chung JS, Lee B, Yu H, Evans NWD (2022) AASIST: audio anti-spoofing using integrated spectro-temporal graph attention networks. In: IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2022, Virtual and Singapore, 23-27 May 2022, IEEE, pp 6367\u20136371. https:\/\/doi.org\/10.1109\/ICASSP43922.2022.9747766","DOI":"10.1109\/ICASSP43922.2022.9747766"},{"key":"6272_CR12","doi-asserted-by":"publisher","unstructured":"Liu X, Liu M, Wang L, Lee KA, Zhang H, Dang J (2023) Leveraging positional-related local-global dependency for synthetic speech detection. In: IEEE International Conference on Acoustics, Speech and Signal Processing ICASSP 2023, Rhodes Island, Greece, June 4-10, 2023, IEEE, pp 1\u20135. https:\/\/doi.org\/10.1109\/ICASSP49357.2023.10096278","DOI":"10.1109\/ICASSP49357.2023.10096278"},{"issue":"4","key":"6272_CR13","doi-asserted-by":"publisher","first-page":"3974","DOI":"10.1007\/S10489-022-03766-Z","volume":"53","author":"M Masood","year":"2023","unstructured":"Masood M, Nawaz M, Malik KM, Javed A, Irtaza A, Malik H (2023) Deepfakes generation and detection: state-of-the-art, open challenges, countermeasures, and way forward. Appl Intell 53(4):3974\u20134026. https:\/\/doi.org\/10.1007\/S10489-022-03766-Z","journal-title":"Appl Intell"},{"issue":"11","key":"6272_CR14","doi-asserted-by":"publisher","first-page":"2884","DOI":"10.1109\/TIFS.2018.2833032","volume":"13","author":"X Wu","year":"2018","unstructured":"Wu X, He R, Sun Z, Tan T (2018) A light cnn for deep face representation with noisy labels. IEEE Trans Inf Forensics Secur 13(11):2884\u20132896","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"6272_CR15","doi-asserted-by":"publisher","unstructured":"Kawa P, Plata M, Syga P (2022) Specrnet: Towards faster and more accessible audio deepfake detection. In: IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2022, Wuhan, China, December 9-11, 2022, IEEE, pp 792\u2013799. https:\/\/doi.org\/10.1109\/TRUSTCOM56396.2022.00111","DOI":"10.1109\/TRUSTCOM56396.2022.00111"},{"key":"6272_CR16","doi-asserted-by":"crossref","unstructured":"Zhou Y, Lim S-N (2021) Joint audio-visual deepfake detection. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 14800\u201314809","DOI":"10.1109\/ICCV48922.2021.01453"},{"key":"6272_CR17","doi-asserted-by":"crossref","unstructured":"Jung J-W, Heo H-S, Tak H, Shim H-J, Chung JS, Lee B-J, Yu H-J, Evans N (2022) Aasist: Audio anti-spoofing using integrated spectro-temporal graph attention networks. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 6367\u20136371","DOI":"10.1109\/ICASSP43922.2022.9747766"},{"key":"6272_CR18","doi-asserted-by":"publisher","unstructured":"Tak H, Patino J, Todisco M, Nautsch A, Evans NWD, Larcher A (2021) End-to-end anti-spoofing with rawnet2. In: IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2021, Toronto, ON, Canada, June 6-11, 2021, IEEE, pp 6369\u20136373. https:\/\/doi.org\/10.1109\/ICASSP39728.2021.9414234","DOI":"10.1109\/ICASSP39728.2021.9414234"},{"key":"6272_CR19","doi-asserted-by":"publisher","first-page":"101308","DOI":"10.1016\/J.CSL.2021.101308","volume":"72","author":"A Wali","year":"2022","unstructured":"Wali A, Alamgir Z, Karim S, Fawaz A, Ali MB, Adan M, Mujtaba M (2022) Generative adversarial networks for speech processing: a review. Comput Speech Lang 72:101308. https:\/\/doi.org\/10.1016\/J.CSL.2021.101308","journal-title":"Comput Speech Lang"},{"key":"6272_CR20","doi-asserted-by":"crossref","unstructured":"AlBadawy EA, Gibiansky A, He Q, Wu J, Chang M-C, Lyu S (2022) Vocbench: a neural vocoder benchmark for speech synthesis. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 881\u2013885","DOI":"10.1109\/ICASSP43922.2022.9746698"},{"key":"6272_CR21","unstructured":"Kumar K, Kumar R, De Boissiere T, Gestin L, Teoh WZ, Sotelo J, Br\u00e9bisson A, Bengio Y, Courville AC (2019) Melgan: generative adversarial networks for conditional waveform synthesis. Advan Neural Inform Process Syst 32"},{"key":"6272_CR22","first-page":"17022","volume":"33","author":"J Kong","year":"2020","unstructured":"Kong J, Kim J, Bae J (2020) Hifi-gan: Generative adversarial networks for efficient and high fidelity speech synthesis. Adv Neural Inf Process Syst 33:17022\u201317033","journal-title":"Adv Neural Inf Process Syst"},{"key":"6272_CR23","doi-asserted-by":"crossref","unstructured":"Mustafa A, Pia N, Fuchs G (2021) Stylemelgan: an efficient high-fidelity adversarial vocoder with temporal adaptive normalization. In: ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 6034\u20136038","DOI":"10.1109\/ICASSP39728.2021.9413605"},{"key":"6272_CR24","doi-asserted-by":"crossref","unstructured":"Yamamoto R, Song E, Kim J-M (2020) Parallel wavegan: a fast waveform generation model based on generative adversarial networks with multi-resolution spectrogram. In: ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 6199\u20136203","DOI":"10.1109\/ICASSP40776.2020.9053795"},{"key":"6272_CR25","doi-asserted-by":"crossref","unstructured":"Yang G, Yang S, Liu K, Fang P, Chen W, Xie L (2021) Multi-band melgan: faster waveform generation for high-quality text-to-speech. In: 2021 IEEE Spoken Language Technology Workshop (SLT), IEEE, pp 492\u2013498","DOI":"10.1109\/SLT48900.2021.9383551"},{"key":"6272_CR26","doi-asserted-by":"crossref","unstructured":"Yan X, Yi J, Tao J, Wang C, Ma H, Wang T, Wang S, Fu R (2022) An initial investigation for detecting vocoder fingerprints of fake audio. In: Proceedings of the 1st international workshop on deepfake detection for audio multimedia, pp 61\u201368","DOI":"10.1145\/3552466.3556525"},{"key":"6272_CR27","doi-asserted-by":"publisher","unstructured":"Sun C, Jia S, Hou S, Lyu S (2023) Ai-synthesized voice detection using neural vocoder artifacts. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023 - Workshops, Vancouver, BC, Canada, June 17-24, 2023, IEEE, pp 904\u2013912. https:\/\/doi.org\/10.1109\/CVPRW59228.2023.00097","DOI":"10.1109\/CVPRW59228.2023.00097"},{"issue":"6","key":"6272_CR28","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1145\/3630751","volume":"20","author":"F Li","year":"2024","unstructured":"Li F, Chen Y, Liu H, Zhao Z, Yao Y, Liao X (2024) Vocoder detection of spoofing speech based on GAN fingerprints and domain generalization. ACM Trans Multim Comput Commun Appl 20(6):157\u2013115720. https:\/\/doi.org\/10.1145\/3630751","journal-title":"ACM Trans Multim Comput Commun Appl"},{"key":"6272_CR29","doi-asserted-by":"publisher","unstructured":"Deng J, Ren Y, Zhang T, Zhu H, Sun Z (2024) Vfd-net: Vocoder fingerprints detection for fake audio. In: ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp 12151\u201312155. https:\/\/doi.org\/10.1109\/ICASSP48485.2024.10446798","DOI":"10.1109\/ICASSP48485.2024.10446798"},{"key":"6272_CR30","doi-asserted-by":"publisher","first-page":"839","DOI":"10.1109\/TASLP.2020.2970241","volume":"28","author":"Y Ai","year":"2020","unstructured":"Ai Y, Ling Z-H (2020) A neural vocoder with hierarchical generation of amplitude and phase spectra for statistical parametric speech synthesis. IEEE\/ACM Trans Audio Speech Language Process 28:839\u2013851","journal-title":"IEEE\/ACM Trans Audio Speech Language Process"},{"key":"6272_CR31","unstructured":"Ren Y, Ruan Y, Tan X, Qin T, Zhao S, Zhao Z, Liu T (2019) Fastspeech: Fast, robust and controllable text to speech. In: Wallach HM, Larochelle H, Beygelzimer A, d\u2019Alch\u00e9-Buc F, Fox EB, Garnett R (eds) Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada, pp. 3165\u20133174. https:\/\/proceedings.neurips.cc\/paper\/2019\/hash\/f63f65b503e22cb970527f23c9ad7db1-Abstract.html"},{"key":"6272_CR32","doi-asserted-by":"crossref","unstructured":"Prenger R, Valle R, Catanzaro B (2019) Waveglow: a flow-based generative network for speech synthesis. In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 3617\u20133621","DOI":"10.1109\/ICASSP.2019.8683143"},{"key":"6272_CR33","unstructured":"Kong Z, Ping W, Huang J, Zhao K, Catanzaro B (2021) Diffwave: A versatile diffusion model for audio synthesis. In: 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021. OpenReview.net. https:\/\/openreview.net\/forum?id=a-xFK8Ymz5J"},{"key":"6272_CR34","unstructured":"Chen N, Zhang Y, Zen H, Weiss RJ, Norouzi M, Chan W (2021) Wavegrad: Estimating gradients for waveform generation. In: 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021. OpenReview.net. https:\/\/openreview.net\/forum?id=NsMLjcFaO8O"},{"key":"6272_CR35","unstructured":"Oord A, Dieleman S, Zen H, Simonyan K, Vinyals O, Graves A, Kalchbrenner N, Senior AW, Kavukcuoglu K (2016) Wavenet: a generative model for raw audio. In: The 9th ISCA Speech Synthesis Workshop, Sunnyvale, CA, USA, 13-15 September 2016, ISCA, p 125. http:\/\/www.isca-speech.org\/archive\/SSW_2016\/abstracts\/ssw9_DS-4_van_den_Oord.html"},{"key":"6272_CR36","unstructured":"Kalchbrenner N, Elsen E, Simonyan K, Noury S, Casagrande N, Lockhart E, Stimberg F, Oord A, Dieleman S, Kavukcuoglu K (2018) Efficient neural audio synthesis. In: Dy JG, Krause A (eds) Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsm\u00e4ssan, Stockholm, Sweden, July 10-15, 2018. Proceedings of Machine Learning Research, vol 80, PMLR, pp 2415\u20132424. http:\/\/proceedings.mlr.press\/v80\/kalchbrenner18a.html"},{"key":"6272_CR37","doi-asserted-by":"publisher","unstructured":"Bak T, Lee J, Bae H, Yang J, Bae J, Joo Y (2023) Avocodo: Generative adversarial network for artifact-free vocoder. In: Williams B, Chen Y, Neville J (eds) Thirty-Seventh AAAI Conference on Artificial Intelligence, AAAI 2023, Thirty-Fifth Conference on Innovative Applications of ArtificialIntelligence, IAAI 2023, Thirteenth Symposium on Educational Advances in Artificial Intelligence, EAAI 2023, Washington, DC, USA, February 7-14, 2023, AAAI Press, pp 12562\u201312570. https:\/\/doi.org\/10.1609\/AAAI.V37I11.26479","DOI":"10.1609\/AAAI.V37I11.26479"},{"key":"6272_CR38","unstructured":"Lee S, Ping W, Ginsburg B, Catanzaro B, Yoon S (2023) Bigvgan: A universal neural vocoder with large-scale training. In: The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023. OpenReview.net. https:\/\/openreview.net\/pdf?id=iTtGCMDEzS_"},{"key":"6272_CR39","unstructured":"Khalid H, Tariq S, Kim M, Woo SS (2021) Fakeavceleb: a novel audio-video multimodal deepfake dataset. In: Vanschoren J, Yeung S (eds) Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, NeurIPS Datasets and Benchmarks 2021, December 2021, Virtual. https:\/\/datasets-benchmarks-proceedings.neurips.cc\/paper\/2021\/hash\/d9d4f495e875a2e075a1a4a6e1b9770f-Abstract-round2.html"},{"key":"6272_CR40","doi-asserted-by":"publisher","first-page":"110124","DOI":"10.1016\/J.ASOC.2023.110124","volume":"136","author":"H Ilyas","year":"2023","unstructured":"Ilyas H, Javed A, Malik KM (2023) Avfakenet: a unified end-to-end dense swin transformer deep learning model for audio-visual deepfakes detection. Appl Soft Comput 136:110124. https:\/\/doi.org\/10.1016\/J.ASOC.2023.110124","journal-title":"Appl Soft Comput"},{"key":"6272_CR41","doi-asserted-by":"publisher","unstructured":"Ma H, Yi J, Tao J, Bai Y, Tian Z, Wang C (2021) Continual learning for fake audio detection. In: Hermansky H, Cernock\u00fd H, Burget L, Lamel L, Scharenborg O, Motl\u00edcek P (eds) Interspeech 2021, 22nd Annual Conference of the International Speech Communication Association, Brno, Czechia, 30 August - 3 September 2021, ISCA, pp 886\u2013890. https:\/\/doi.org\/10.21437\/INTERSPEECH.2021-794","DOI":"10.21437\/INTERSPEECH.2021-794"},{"key":"6272_CR42","unstructured":"Zhang X, Yi J, Tao J, Wang C, Zhang CY (2023) Do you remember? overcoming catastrophic forgetting for fake audio detection. In: Krause A, Brunskill E, Cho K, Engelhardt B, Sabato S, Scarlett J (eds) International Conference on Machine Learning, ICML 2023, 23-29 July 2023, Honolulu, Hawaii, USA. Proceedings of Machine Learning Research, vol 202, PMLR, pp 41819\u201341831. https:\/\/proceedings.mlr.press\/v202\/zhang23au.html"},{"key":"6272_CR43","doi-asserted-by":"publisher","unstructured":"Chen T, Kumar A, Nagarsheth P, Sivaraman G, Khoury E (2020) Generalization of audio deepfake detection. In: Lee K, Koshinaka T, Shinoda K (eds) Odyssey 2020: The Speaker and Language Recognition Workshop, 1-5 November 2020, Tokyo, Japan, ISCA, pp 132\u2013137. https:\/\/doi.org\/10.21437\/ODYSSEY.2020-19","DOI":"10.21437\/ODYSSEY.2020-19"},{"key":"6272_CR44","doi-asserted-by":"publisher","unstructured":"Ding S, Zhang Y, Duan Z (2023) SAMO: speaker attractor multi-center one-class learning for voice anti-spoofing. In: IEEE International Conference on Acoustics, Speech and Signal Processing ICASSP 2023, Rhodes Island, Greece, June 4-10, 2023, IEEE, pp 1\u20135. https:\/\/doi.org\/10.1109\/ICASSP49357.2023.10094704","DOI":"10.1109\/ICASSP49357.2023.10094704"},{"key":"6272_CR45","doi-asserted-by":"publisher","unstructured":"Wang X, Yamagishi J (2021) A comparative study on recent neural spoofing countermeasures for synthetic speech detection. In: Hermansky H, Cernock\u00fd H, Burget L, Lamel L, Scharenborg O, Motl\u00edcek P (eds) Interspeech 2021, 22nd Annual Conference of the International Speech Communication Association, Brno, Czechia, 30 August - 3 September 2021, ISCA, pp 4259\u20134263. https:\/\/doi.org\/10.21437\/INTERSPEECH.2021-702","DOI":"10.21437\/INTERSPEECH.2021-702"},{"key":"6272_CR46","doi-asserted-by":"publisher","unstructured":"Lv Z, Zhang S, Tang K, Hu P (2022) Fake audio detection based on unsupervised pretraining models. In: IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2022, Virtual and Singapore, 23-27 May 2022, IEEE, pp 9231\u20139235. https:\/\/doi.org\/10.1109\/ICASSP43922.2022.9747605","DOI":"10.1109\/ICASSP43922.2022.9747605"},{"key":"6272_CR47","doi-asserted-by":"publisher","unstructured":"Sun C, AlBadawy E, Davison TF, Robinson SR, Chang M-C, Lyu S (2024) In: Nowroozi E, Kallas K, Jolfaei A (eds) Using Vocoder Artifacts For Audio Deepfakes Detection, Springer, Cham, pp 263\u2013282. https:\/\/doi.org\/10.1007\/978-3-031-49803-9_11","DOI":"10.1007\/978-3-031-49803-9_11"},{"key":"6272_CR48","doi-asserted-by":"publisher","unstructured":"Zhang CY, Yi J, Tao J, Wang C, Yan X (2023) Distinguishing neural speech synthesis models through fingerprints in speech waveforms. arXiv:2309.06780. https:\/\/doi.org\/10.48550\/ARXIV.2309.06780","DOI":"10.48550\/ARXIV.2309.06780"},{"key":"6272_CR49","unstructured":"Kim C, Ren Y, Yang Y (2021) Decentralized attribution of generative models. In: 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021. OpenReview.net. https:\/\/openreview.net\/forum?id=_kxlwvhOodK"},{"key":"6272_CR50","doi-asserted-by":"crossref","unstructured":"Yu N, Skripniuk V, Abdelnabi S, Fritz M (2021) Artificial fingerprinting for generative models: Rooting deepfake attribution in training data. In: 2021 IEEE\/CVF International Conference on Computer Vision, ICCV 2021, Montreal, QC, Canada, October 10-17, 2021, pp 14448\u201314457","DOI":"10.1109\/ICCV48922.2021.01418"},{"key":"6272_CR51","doi-asserted-by":"crossref","unstructured":"Joslin M, Hao S (2020) Attributing and detecting fake images generated by known gans. In: 2020 IEEE Security and Privacy Workshops (SPW), IEEE, pp 8\u201314","DOI":"10.1109\/SPW50608.2020.00019"},{"key":"6272_CR52","doi-asserted-by":"crossref","unstructured":"Qian Y, Yin G, Sheng L, Chen Z, Shao J (2020) Thinking in frequency: Face forgery detection by mining frequency-aware clues. In: Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part XII, Springer, pp 86\u2013103","DOI":"10.1007\/978-3-030-58610-2_6"},{"key":"6272_CR53","unstructured":"Frank J, Eisenhofer T, Sch\u00f6nherr L, Fischer A, Kolossa D, Holz T (2020) Leveraging frequency analysis for deep fake image recognition. In: International Conference on Machine Learning, PMLR, pp 3247\u20133258"},{"key":"6272_CR54","doi-asserted-by":"crossref","unstructured":"Marra F, Gragnaniello D, Verdoliva L, Poggi G (2019) Do gans leave artificial fingerprints? In: 2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR), IEEE, pp 506\u2013511","DOI":"10.1109\/MIPR.2019.00103"},{"key":"6272_CR55","doi-asserted-by":"publisher","unstructured":"Tak H, Todisco M, Wang X, Jung J, Yamagishi J, Evans NWD (2022) Automatic speaker verification spoofing and deepfake detection using wav2vec 2.0 and data augmentation. In: Zheng TF (ed) Odyssey 2022: The Speaker and Language Recognition Workshop, 28 June - 1 July 2022, Beijing, China, ISCA, pp 112\u2013119. https:\/\/doi.org\/10.21437\/ODYSSEY.2022-16","DOI":"10.21437\/ODYSSEY.2022-16"},{"key":"6272_CR56","doi-asserted-by":"publisher","unstructured":"Lavrentyeva G, Novoselov S, Tseren A, Volkova M, Gorlanov A, Kozlov A (2019) STC antispoofing systems for the asvspoof2019 challenge. In: Kubin G, Kacic Z (eds) Interspeech 2019, 20th Annual Conference of the International Speech Communication Association, Graz, Austria, 15-19 September 2019, ISCA, pp 1033\u20131037. https:\/\/doi.org\/10.21437\/INTERSPEECH.2019-1768","DOI":"10.21437\/INTERSPEECH.2019-1768"},{"key":"6272_CR57","doi-asserted-by":"publisher","first-page":"1579","DOI":"10.1109\/TIFS.2020.3039045","volume":"16","author":"A Gomez-Alanis","year":"2020","unstructured":"Gomez-Alanis A, Gonzalez-Lopez JA, Dubagunta SP, Peinado AM, Doss MM (2020) On joint optimization of automatic speaker verification and anti-spoofing in the embedding space. IEEE Trans Inform Forensics Secur 16:1579\u20131593","journal-title":"IEEE Trans Inform Forensics Secur"},{"key":"6272_CR58","unstructured":"Radford A, Kim JW, Xu T, Brockman G, McLeavey C, Sutskever I (2023) Robust speech recognition via large-scale weak supervision. In: International conference on machine learning, PMLR, pp 28492\u201328518"},{"key":"6272_CR59","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"6272_CR60","unstructured":"Stoller D, Ewert S, Dixon S (2018) Wave-u-net: a multi-scale neural network for end-to-end audio source separation. In: G\u00f3mez E, Hu X, Humphrey E, Benetos E (eds) Proceedings of the 19th International Society for Music Information Retrieval Conference, ISMIR 2018, Paris, France, September 23-27, 2018, pp 334\u2013340. http:\/\/ismir2018.ircam.fr\/doc\/pdfs\/205_Paper.pdf"},{"key":"6272_CR61","doi-asserted-by":"crossref","unstructured":"Snyder D, Garcia-Romero D, Sell G, Povey D, Khudanpur S (2018) X-vectors: robust dnn embeddings for speaker recognition. In: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 5329\u20135333","DOI":"10.1109\/ICASSP.2018.8461375"},{"key":"6272_CR62","doi-asserted-by":"crossref","unstructured":"Jung J-W, Kim S-B, Shim H-J, Kim J-H, Yu H-J (2020) Improved rawnet with feature map scaling for text-independent speaker verification using raw waveforms. Proc Interspeech:3583\u20133587","DOI":"10.21437\/Interspeech.2020-1011"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-025-06272-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-025-06272-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-025-06272-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T19:38:59Z","timestamp":1758310739000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-025-06272-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,21]]},"references-count":62,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2025,5]]}},"alternative-id":["6272"],"URL":"https:\/\/doi.org\/10.1007\/s10489-025-06272-0","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"type":"print","value":"0924-669X"},{"type":"electronic","value":"1573-7497"}],"subject":[],"published":{"date-parts":[[2025,4,21]]},"assertion":[{"value":"21 April 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All datasets used in this paper are public datasets, which can be downloaded through public channels upon request.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical and informed consent for data used"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interests"}}],"article-number":"673"}}