{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T19:30:06Z","timestamp":1773343806405,"version":"3.50.1"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,9,24]],"date-time":"2022-09-24T00:00:00Z","timestamp":1663977600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,9,24]],"date-time":"2022-09-24T00:00:00Z","timestamp":1663977600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100006450","name":"University of G\u00e4vle","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100006450","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Cloud Comp"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>In our everyday lives, we communicate with each other using several means and channels of communication, as communication is crucial in the lives of humans. Listening and speaking are the primary forms of communication. For listening and speaking, the human voice is indispensable. Voice communication is the simplest type of communication. The Automatic Speaker Verification (ASV) system verifies users with their voices. These systems are susceptible to voice spoofing attacks - logical and physical access attacks. Recently, there has been a notable development in the detection of these attacks. Attackers use enhanced gadgets to record users\u2019 voices, replay them for the ASV system, and be granted access for harmful purposes. In this work, we propose a secure voice spoofing countermeasure to detect voice replay attacks. We enhanced the ASV system security by building a spoofing countermeasure dependent on the decomposed signals that consist of prominent information. We used two main features\u2014 the Gammatone Cepstral Coefficients and Mel-Frequency Cepstral Coefficients\u2014 for the audio representation. For the classification of the features, we used Bi-directional Long-Short Term Memory Network in the cloud, a deep learning classifier. We investigated numerous audio features and examined each feature\u2019s capability to obtain the most vital details from the audio for it to be labelled genuine or a spoof speech. Furthermore, we use various machine learning algorithms to illustrate the superiority of our system compared to the traditional classifiers. The results of the experiments were classified according to the parameters of accuracy, precision rate, recall, F1-score, and Equal Error Rate (EER). The results were 97%, 100%, 90.19% and 94.84%, and 2.95%, respectively.<\/jats:p>","DOI":"10.1186\/s13677-022-00306-5","type":"journal-article","created":{"date-parts":[[2022,9,24]],"date-time":"2022-09-24T15:15:59Z","timestamp":1664032559000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Voice spoofing countermeasure for voice replay attacks using deep learning"],"prefix":"10.1186","volume":"11","author":[{"given":"Jincheng","family":"Zhou","sequence":"first","affiliation":[]},{"given":"Tao","family":"Hai","sequence":"additional","affiliation":[]},{"given":"Dayang N. A.","family":"Jawawi","sequence":"additional","affiliation":[]},{"given":"Dan","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Ebuka","family":"Ibeke","sequence":"additional","affiliation":[]},{"given":"Cresantus","family":"Biamba","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,24]]},"reference":[{"issue":"5","key":"306_CR1","doi-asserted-by":"publisher","first-page":"5556","DOI":"10.1002\/cpe.5556","volume":"32","author":"Y Xu","year":"2020","unstructured":"Xu Y, Zeng Q, Wang G, Zhang C, Ren J, Zhang Y (2020) An efficient privacy-enhanced attribute-based access control mechanism. Concurr Comput Pract Experience 32(5):5556","journal-title":"Concurr Comput Pract Experience"},{"key":"306_CR2","doi-asserted-by":"crossref","unstructured":"Mittal M, Iwendi C (2019) A survey on energy-aware wireless sensor routing protocols. EAI Endorsed Trans Energy Web 6(24).\u00a0https:\/\/eudl.eu\/doi\/10.4108\/eai.11-6-2019.160835","DOI":"10.4108\/eai.11-6-2019.160835"},{"issue":"15","key":"306_CR3","doi-asserted-by":"publisher","first-page":"17373","DOI":"10.1109\/JSEN.2021.3080217","volume":"21","author":"S Ponnan","year":"2021","unstructured":"Ponnan S, Saravanan AK, Iwendi C, Ibeke E, Srivastava G (2021) An artificial intelligence-based quorum system for the improvement of the lifespan of sensor networks. IEEE Sensors J 21(15):17373\u201317385.","journal-title":"IEEE Sensors J"},{"issue":"2","key":"306_CR4","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1109\/TIFS.2006.873653","volume":"1","author":"AK Jain","year":"2006","unstructured":"Jain AK, Ross A, Pankanti S (2006) Biometrics: a tool for information security. IEEE Trans Inf Forensic Secur 1(2):125\u2013143.","journal-title":"IEEE Trans Inf Forensic Secur"},{"key":"306_CR5","doi-asserted-by":"crossref","unstructured":"Naika R (2018) An overview of automatic speaker verification system. Intell Comput Inf Commun:603\u2013610.\u00a0https:\/\/link.springer.com\/chapter\/10.1007\/978-981-10-7245-1_59","DOI":"10.1007\/978-981-10-7245-1_59"},{"key":"306_CR6","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1016\/j.specom.2014.10.005","volume":"66","author":"Z Wu","year":"2015","unstructured":"Wu Z, Evans N, Kinnunen T, Yamagishi J, Alegre F, Li H (2015) Spoofing and countermeasures for speaker verification: A survey. Speech Commun 66:130\u2013153.","journal-title":"Speech Commun"},{"key":"306_CR7","doi-asserted-by":"crossref","unstructured":"Korshunov P, Marcel S (2016) Cross-database evaluation of audio-based spoofing detection systems In: Interspeech. https:\/\/infoscience.epfl.ch\/record\/219837?ln=en","DOI":"10.21437\/Interspeech.2016-1326"},{"key":"306_CR8","doi-asserted-by":"crossref","unstructured":"Wu Z, Kinnunen T, Evans N, Yamagishi J, Hanil\u00e7i C, Sahidullah M, Sizov A (2015) Asvspoof 2015: the first automatic speaker verification spoofing and countermeasures challenge In: Sixteenth Annual Conference of the International Speech Communication Association. https:\/\/www.eurecom.fr\/publication\/4573","DOI":"10.21437\/Interspeech.2015-462"},{"key":"306_CR9","unstructured":"Korshunov P, Marcel S, Muckenhirn H, Gon\u00e7alves AR, Mello AS, Violato RV, Simoes FO, Neto MU, de Assis Angeloni M, Stuchi JA, et al (2016) Overview of btas 2016 speaker anti-spoofing competition In: 2016 IEEE 8th International Conference on Biometrics Theory, Applications and Systems (BTAS), 1\u20136. IEEE. https:\/\/ieeexplore.ieee.org\/abstract\/document\/7791200?casa_token=W9RbLt8WBD0AAAAA:b7UL3xnAGjtfvUxtocPZXg4YdSkVaPE4Ezy6KQsAuBYRiFlPVlLN4d6pubtUml1Q9ifpqYjKBgk"},{"key":"306_CR10","doi-asserted-by":"crossref","unstructured":"Kinnunen T, Sahidullah M, Delgado H, Todisco M, Evans N, Yamagishi J, Lee KA (2017) The asvspoof 2017 challenge: Assessing the limits of replay spoofing attack detection. https:\/\/www.isca-speech.org\/archive\/interspeech_2017\/kinnunen17_interspeech.html","DOI":"10.21437\/Interspeech.2017-1111"},{"key":"306_CR11","doi-asserted-by":"crossref","unstructured":"Palanivinayagam A, Gopal SS, Bhattacharya S, Anumbe N, Ibeke E, Biamba C (2021) An optimized machine learning and big data approach to crime detection. Wirel Commun Mob Comput 2021.\u00a0https:\/\/www.hindawi.com\/journals\/wcmc\/2021\/5291528\/","DOI":"10.1155\/2021\/5291528"},{"key":"306_CR12","doi-asserted-by":"publisher","first-page":"2195","DOI":"10.1109\/TASLP.2020.3009494","volume":"28","author":"T Kinnunen","year":"2020","unstructured":"Kinnunen T, Delgado H, Evans N, Lee KA, Vestman V, Nautsch A, Todisco M, Wang X, Sahidullah M, Yamagishi J, et al (2020) Tandem assessment of spoofing countermeasures and automatic speaker verification: Fundamentals. IEEE\/ACM Trans Audio Speech Lang Process 28:2195\u20132210.","journal-title":"IEEE\/ACM Trans Audio Speech Lang Process"},{"key":"306_CR13","doi-asserted-by":"crossref","unstructured":"Mittal M, Saraswat LK, Iwendi C, Anajemba JH (2019) A neuro-fuzzy approach for intrusion detection in energy efficient sensor routing In: 2019 4th International Conference on Internet of Things: Smart Innovation and Usages (IoT-SIU), 1\u20135.. IEEE.","DOI":"10.1109\/IoT-SIU.2019.8777501"},{"key":"306_CR14","doi-asserted-by":"publisher","first-page":"274","DOI":"10.1016\/j.comcom.2021.09.029","volume":"181","author":"SA Latif","year":"2022","unstructured":"Latif SA, Wen FBX, Iwendi C, Li-li FW, Mohsin SM, Han Z, Band SS (2022) Ai-empowered, blockchain and sdn integrated security architecture for IoT network of cyber physical systems. Comput Commun 181:274\u2013283.","journal-title":"Comput Commun"},{"key":"306_CR15","doi-asserted-by":"crossref","unstructured":"Iwendi C, Srivastava G, Khan S, Maddikunta PKR (2020) Cyberbullying detection solutions based on deep learning architectures. Multimedia Systems:1\u201314.\u00a0https:\/\/link.springer.com\/article\/10.1007\/s00530-020-00701-5","DOI":"10.1007\/s00530-020-00701-5"},{"issue":"12","key":"306_CR16","doi-asserted-by":"publisher","first-page":"2558","DOI":"10.1002\/spe.2797","volume":"51","author":"C Iwendi","year":"2021","unstructured":"Iwendi C, Maddikunta PKR, Gadekallu TR, Lakshmanna K, Bashir AK, Piran MJ (2021) A metaheuristic optimization approach for energy efficiency in the IoT networks. Softw Pract Experience 51(12):2558\u20132571.","journal-title":"Softw Pract Experience"},{"key":"306_CR17","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1016\/j.specom.2016.10.002","volume":"85","author":"C Hanilci","year":"2016","unstructured":"Hanilci C, Kinnunen T, Sahidullah M, Sizov A (2016) Spoofing detection goes noisy: An analysis of synthetic speech detection in the presence of additive noise. Speech Comm 85:83\u201397.","journal-title":"Speech Comm"},{"key":"306_CR18","doi-asserted-by":"publisher","first-page":"116597","DOI":"10.1016\/j.eswa.2022.116597","volume":"195","author":"K Bharath","year":"2022","unstructured":"Bharath K, Kumar MR (2022) New replay attack detection using iterative adaptive inverse filtering and high frequency band. Expert Syst Appl 195:116597.","journal-title":"Expert Syst Appl"},{"key":"306_CR19","doi-asserted-by":"publisher","first-page":"101281","DOI":"10.1016\/j.csl.2021.101281","volume":"72","author":"AT Patil","year":"2022","unstructured":"Patil AT, Acharya R, Patil HA, Guido RC (2022) Improving the potential of enhanced teager energy cepstral coefficients (etecc) for replay attack detection. Comput Speech Lang 72:101281.","journal-title":"Comput Speech Lang"},{"key":"306_CR20","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1016\/j.specom.2021.06.004","volume":"132","author":"T Gunendradasan","year":"2021","unstructured":"Gunendradasan T, Ambikairajah E, Epps J, Sethu V, Li H (2021) An adaptive transmission line cochlear model based front-end for replay attack detection. Speech Comm 132:114\u2013122.","journal-title":"Speech Comm"},{"key":"306_CR21","doi-asserted-by":"publisher","first-page":"3524","DOI":"10.1109\/TIFS.2021.3082303","volume":"16","author":"M Aljasem","year":"2021","unstructured":"Aljasem M, Irtaza A, Malik H, Saba N, Javed A, Malik KM, Meharmohammadi M (2021) Secure automatic speaker verification (sasv) system through sm-altp features and asymmetric bagging. IEEE Trans Inf Forensic Secur 16:3524\u20133537.","journal-title":"IEEE Trans Inf Forensic Secur"},{"issue":"3","key":"306_CR22","doi-asserted-by":"publisher","first-page":"12670","DOI":"10.1111\/exsy.12670","volume":"38","author":"B Nasersharif","year":"2021","unstructured":"Nasersharif B, Yazdani M (2021) Evolutionary fusion of classifiers trained on linear prediction based features for replay attack detection. Expert Syst 38(3):12670.","journal-title":"Expert Syst"},{"key":"306_CR23","doi-asserted-by":"crossref","unstructured":"Yue L, Cao C, Li Y, Li J, Liu Q (2021) Liveear: An efficient and easy-to-use liveness detection system for voice assistants In: Journal of Physics: Conference Series, vol. 1871, 012046. IOP Publishing. https:\/\/iopscience.iop.org\/article\/10.1088\/1742-6596\/1871\/1\/012046\/meta","DOI":"10.1088\/1742-6596\/1871\/1\/012046"},{"key":"306_CR24","doi-asserted-by":"publisher","first-page":"108283","DOI":"10.1016\/j.apacoust.2021.108283","volume":"183","author":"A Javed","year":"2021","unstructured":"Javed A, Malik KM, Irtaza A, Malik H (2021) Towards protecting cyber-physical and IoT systems from single-and multi-order voice spoofing attacks. Appl Acoust 183:108283.","journal-title":"Appl Acoust"},{"key":"306_CR25","first-page":"275","volume":"29","author":"R Yaguchi","year":"2021","unstructured":"Yaguchi R, Shiota S, Ono N, Kiya H (2021) Replay attack detection based on spatial and spectral features of stereo signal. J Inf Process 29:275\u2013282.","journal-title":"J Inf Process"},{"issue":"2","key":"306_CR26","doi-asserted-by":"publisher","first-page":"274","DOI":"10.3390\/sym14020274","volume":"14","author":"L Wei","year":"2022","unstructured":"Wei L, Long Y, Wei H, Li Y (2022) New acoustic features for synthetic and replay spoofing attack detection. Symmetry 14(2):274.","journal-title":"Symmetry"},{"issue":"4","key":"306_CR27","doi-asserted-by":"publisher","first-page":"2946","DOI":"10.1109\/TNSE.2021.3055762","volume":"8","author":"Y Xu","year":"2021","unstructured":"Xu Y, Yan X, Wu Y, Hu Y, Liang W, Zhang J (2021) Hierarchical bidirectional rnn for safety-enhanced b5g heterogeneous networks. IEEE Trans Netw Sci Eng 8(4):2946\u20132957.","journal-title":"IEEE Trans Netw Sci Eng"},{"key":"306_CR28","doi-asserted-by":"crossref","unstructured":"Xu Y, Liu Z, Zhang C, Ren J, Zhang Y, Shen X (2021) Blockchain-based trustworthy energy dispatching approach for high renewable energy penetrated power systems. IEEE Internet Things J.\u00a0https:\/\/ieeexplore.ieee.org\/document\/9560154","DOI":"10.1109\/JIOT.2021.3117924"},{"key":"306_CR29","unstructured":"Prajapati GP, Kamble MR, Patil HA (2021) Energy separation based features for replay spoof detection for voice assistant In: 2020 28th European Signal Processing Conference (EUSIPCO), 386\u2013390. IEEE. https:\/\/ieeexplore.ieee.org\/abstract\/document\/9287577?casa_token=GZiV_1nQlJ8AAAAA:UYPT7IwwAXHErozDrXJERnHsCg63Ke43hc-btmjYAeEmTeU0ZTeJ8Rq2a73VXF4sknn0JnDg1K0"},{"issue":"2","key":"306_CR30","doi-asserted-by":"publisher","first-page":"497","DOI":"10.1007\/s10772-021-09810-6","volume":"24","author":"K Dutta","year":"2021","unstructured":"Dutta K, Singh M, Pati D (2021) Detection of replay signals using excitation source and shifted cqcc features. Int J Speech Technol 24(2):497\u2013507.","journal-title":"Int J Speech Technol"},{"key":"306_CR31","unstructured":"Meng Y, Li J, Pillari M, Deopujari A, Brennan L, Shamsie H, Zhu H, Tian Y (2022) Your microphone array retains your identity: A robust voice liveness detection system for smart speaker In: USENIX Security. https:\/\/www.usenix.org\/conference\/usenixsecurity22\/presentation\/meng"},{"issue":"2","key":"306_CR32","doi-asserted-by":"publisher","first-page":"1153","DOI":"10.1007\/s40747-021-00565-w","volume":"8","author":"A Mittal","year":"2022","unstructured":"Mittal A, Dua M (2022) Static\u2013dynamic features and hybrid deep learning models based spoof detection system for asv. Compl Intell Syst 8(2):1153\u20131166.","journal-title":"Compl Intell Syst"},{"issue":"7","key":"306_CR33","doi-asserted-by":"publisher","first-page":"8383","DOI":"10.1007\/s11042-018-6834-3","volume":"78","author":"Y Ren","year":"2019","unstructured":"Ren Y, Fang Z, Liu D, Chen C (2019) Replay attack detection based on distortion by loudspeaker for voice authentication. Multimed Tools Appl 78(7):8383\u20138396.","journal-title":"Multimed Tools Appl"},{"key":"306_CR34","doi-asserted-by":"publisher","first-page":"36080","DOI":"10.1109\/ACCESS.2020.2974290","volume":"8","author":"S-H Yoon","year":"2020","unstructured":"Yoon S-H, Koh M-S, Park J-H, Yu H-J (2020) A new replay attack against automatic speaker verification systems. IEEE Access 8:36080\u201336088.","journal-title":"IEEE Access"},{"key":"306_CR35","doi-asserted-by":"crossref","unstructured":"Garg S, Bhilare S, Kanhangad V (2019) Subband analysis for performance improvement of replay attack detection in speaker verification systems In: 2019 IEEE 5th International Conference on Identity, Security, and Behavior Analysis (ISBA), 1\u20137. IEEE.\u00a0https:\/\/ieeexplore.ieee.org\/abstract\/document\/8778535?casa_token=swFCpmqf1sgAAAAA:IMxyoJwsGipHVxdSa2_skF3CyDpsEhI74jQtQrGYwtVwAJKZuwQ1lh_m9YeJOxZJz6urNsR97Q8","DOI":"10.1109\/ISBA.2019.8778535"},{"key":"306_CR36","unstructured":"Gunendradasan T, Irtza S, Ambikairajah E, Epps J (2019) Transmission line cochlear model based am-fm features for replay attack detection In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 6136\u20136140. IEEE. https:\/\/ieeexplore.ieee.org\/abstract\/document\/8682771?casa_token=xwIzDD2oWzEAAAAA:5AuG-q43ii2y_mz5VGTn8TlSf1eMcXK0srIwfU1vX5ZE43wGDzzwcUHG2LWwATPZr7tNs4_F4G8"},{"key":"306_CR37","doi-asserted-by":"publisher","first-page":"152837","DOI":"10.1016\/j.aeue.2019.152837","volume":"110","author":"M Singh","year":"2019","unstructured":"Singh M, Pati D (2019) Usefulness of linear prediction residual for replay attack detection. AEU-Int J Electron Commun 110:152837.","journal-title":"AEU-Int J Electron Commun"},{"key":"306_CR38","doi-asserted-by":"crossref","unstructured":"Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, Yen N-C, Tung CC, Liu HH1998. The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis.\u00a0https:\/\/www.jstor.org\/stable\/53161","DOI":"10.1098\/rspa.1998.0193"},{"key":"306_CR39","unstructured":"Rilling G, Flandrin P, Goncalves P, et al (2003) On empirical mode decomposition and its algorithms In: IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing, vol. 3, 8\u201311. IEEER Grado.\u00a0https:\/\/citeseerx.ist.psu.edu\/viewdoc\/download?doi=10.1.1.586.812&rep=rep1&type=pdf"},{"issue":"12","key":"306_CR40","doi-asserted-by":"publisher","first-page":"2938","DOI":"10.2514\/1.43207","volume":"47","author":"YS Lee","year":"2009","unstructured":"Lee YS, Tsakirtzis S, Vakakis AF, Bergman LA, McFarland DM (2009) Physics-based foundation for empirical mode decomposition. AIAA J 47(12):2938\u20132963.","journal-title":"AIAA J"},{"issue":"3","key":"306_CR41","doi-asserted-by":"publisher","first-page":"821","DOI":"10.1016\/j.ymssp.2010.10.002","volume":"25","author":"R Ricci","year":"2011","unstructured":"Ricci R, Pennacchi P (2011) Diagnostics of gear faults based on emd and automatic selection of intrinsic mode functions. Mech Syst Signal Process 25(3):821\u2013838.","journal-title":"Mech Syst Signal Process"},{"issue":"3","key":"306_CR42","doi-asserted-by":"publisher","first-page":"812","DOI":"10.1016\/j.ymssp.2010.10.007","volume":"25","author":"C Li","year":"2011","unstructured":"Li C, Wang X, Tao Z, Wang Q, Du S (2011) Extraction of time varying information from noisy signals: An approach based on the empirical mode decomposition. Mech Syst Signal Process 25(3):812\u2013820.","journal-title":"Mech Syst Signal Process"},{"issue":"4","key":"306_CR43","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1109\/TASSP.1980.1163420","volume":"28","author":"S Davis","year":"1980","unstructured":"Davis S, Mermelstein P (1980) Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences. IEEE Trans Acoust Speech Sig Process 28(4):357\u2013366.","journal-title":"IEEE Trans Acoust Speech Sig Process"},{"issue":"Part B","key":"306_CR44","first-page":"547","volume":"3","author":"RD Patterson","year":"1996","unstructured":"Patterson RD, Holdsworth J, et al (1996) A functional model of neural activity patterns and auditory images. Adv Speech Hear Lang Process 3(Part B):547\u2013563.","journal-title":"Adv Speech Hear Lang Process"},{"issue":"2","key":"306_CR45","first-page":"289","volume":"13","author":"Y Xu","year":"2019","unstructured":"Xu Y, Ren J, Zhang Y, Zhang C, Shen B, Zhang Y (2019) Blockchain empowered arbitrable data auditing scheme for network storage as a service. IEEE Trans Serv Comput 13(2):289\u2013300.","journal-title":"IEEE Trans Serv Comput"},{"issue":"2","key":"306_CR46","doi-asserted-by":"publisher","first-page":"1202","DOI":"10.1109\/TNSE.2020.2976697","volume":"8","author":"Y Xu","year":"2020","unstructured":"Xu Y, Zhang C, Zeng Q, Wang G, Ren J, Zhang Y (2020) Blockchain-enabled accountability mechanism against information leakage in vertical industry services. IEEE Trans Netw Sci Eng 8(2):1202\u20131213.","journal-title":"IEEE Trans Netw Sci Eng"},{"issue":"3","key":"306_CR47","doi-asserted-by":"publisher","first-page":"1421","DOI":"10.1109\/TETC.2020.3005610","volume":"9","author":"Y Xu","year":"2020","unstructured":"Xu Y, Zhang C, Wang G, Qin Z, Zeng Q (2020) A blockchain-enabled deduplicatable data auditing mechanism for network storage services. IEEE Trans Emerg Top Comput 9(3):1421\u20131432.","journal-title":"IEEE Trans Emerg Top Comput"},{"key":"306_CR48","unstructured":"Yamagishi J, Todisco M, Sahidullah M, Delgado H, Wang X, Evans N, Kinnunen T, Lee KA, Vestman V, Nautsch A (2019) Asvspoof 2019: The 3rd automatic speaker verification spoofing and countermeasures challenge database.\u00a0https:\/\/ieeexplore.ieee.org\/document\/9358099"},{"key":"306_CR49","unstructured":"Das RK, Yang J, Li H (2020) Assessing the scope of generalized countermeasures for anti-spoofing In: ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 6589\u20136593. IEEE.\u00a0https:\/\/ieeexplore.ieee.org\/abstract\/document\/9053086\/?casa_token=t_M6aLgSkwoAAAAA:7m52qVwU913gZOV79c_GPeXg3BjG8DXmK0R-cfYo_1cPpM1zcg6HEop-gcqK8_olpwWsBA0p-Rw"},{"key":"306_CR50","doi-asserted-by":"crossref","unstructured":"Kumar RL, Khan F, Din S, Band SS, Mosavi A, Ibeke E (2021) Recurrent neural network and reinforcement learning model for covid-19 prediction. Front Public Health 9.\u00a0https:\/\/www.frontiersin.org\/articles\/10.3389\/fpubh.2021.744100\/full","DOI":"10.3389\/fpubh.2021.744100"}],"container-title":["Journal of Cloud Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13677-022-00306-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13677-022-00306-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13677-022-00306-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,4]],"date-time":"2024-10-04T15:41:31Z","timestamp":1728056491000},"score":1,"resource":{"primary":{"URL":"https:\/\/journalofcloudcomputing.springeropen.com\/articles\/10.1186\/s13677-022-00306-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,24]]},"references-count":50,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["306"],"URL":"https:\/\/doi.org\/10.1186\/s13677-022-00306-5","relation":{},"ISSN":["2192-113X"],"issn-type":[{"value":"2192-113X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,24]]},"assertion":[{"value":"9 July 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 July 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 September 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 January 2023","order":4,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Update","order":5,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Incorrect Funding note.","order":6,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The research has consent for Ethical Approval and Consent to participate.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"The research has consent by all authors and there is no conflict.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"There is no competing interest.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"51"}}