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The ASVspoof 2017 Challenge: Assessing the Limits of Replay Spoofing Attack Detection
Open accessAbstract
The ASVspoof initiative was created to promote the development of countermeasures which aim to protect automatic speaker verification (ASV) from spoofing attacks. The first community-led, common evaluation held in 2015 focused on countermeasures for speech synthesis and voice conversion spoofing attacks. Arguably, however, it is replay attacks which pose the greatest threat. Such attacks involve the replay of recordings collected from enrolled speakers in order to provoke false alarms and can be mounted with greater ease using everyday consumer devices. ASVspoof 2017, the second in the series, hence focused on the development of replay attack countermeasures. This paper describes the database, protocols and initial findings. The evaluation entailed highly heterogeneous acoustic recording and replay conditions which increased the equal error rate (EER) of a baseline ASV system from 1.76% to 30.71%. Submissions were received from 49 research teams, 20 of which improved upon a baseline replay spoofing detector EER of 24.65%, in terms of replay/non-replay discrimination. While largely successful, the evaluation indicates that the quest for countermeasures which are resilient in the face of variable replay attacks remains very much alive.
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“Additionally, before the ASVspoof Challenge 2017 (Kinnunen et al., 2017; Lavrentyeva et al., 2017), there were only a couple of research papers done on replay attack, and after this challenge, more approaches for this attack were researched (Tom et al., 2018; Pradhan et al., 2019).”
From Audio deepfakes: A survey (2023) · cites this paper as Kinnunen et al., 2017
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Citations
Cites
Speaker Verification Using Adapted Gaussian Mixture Models
Reynolds, D. A., Quatieri, T. F., & Dunn, R. B. (2000) · Digital Signal Processing · doi:10.1006/dspr.1999.0361 · Cited by 4,282
Robust text-independent speaker identification using Gaussian mixture speaker models
Reynolds, D. A., & Rose, R. C. (1995) · IEEE Transactions on Speech and Audio Processing · doi:10.1109/89.365379 · Cited by 2,860
An overview of text-independent speaker recognition: From features to supervectors
Kinnunen, T., & Li, H. (2009) · Speech Communication · doi:10.1016/j.specom.2009.08.009 · Cited by 1,451
Self-citationCalculation of a constant Q spectral transform
Brown, J. T. (1991) · The Journal of the Acoustical Society of America · doi:10.1121/1.400476 · Cited by 925
Speaker Recognition by Machines and Humans: A tutorial review
Hansen, J. H. L., & Hasan, T. (2015) · IEEE Signal Processing Magazine · doi:10.1109/msp.2015.2462851 · Cited by 652
Spoofing and countermeasures for speaker verification: A survey
Wu, Z., Evans, N., Kinnunen, T., Yamagishi, J., Alegre, F., & Li, H. (2015) · Speech Communication · doi:10.1016/j.specom.2014.10.005 · Cited by 601
Self-citationASVspoof 2015: the first automatic speaker verification spoofing and countermeasures challenge
Wu, Z., Kinnunen, T., Evans, N., Yamagishi, J., Hanilçi, C., Sahidullah, M., & Sizov, A. (2015) · Interspeech 2015 · doi:10.21437/interspeech.2015-462 · Cited by 484 · cited 1×
“ASVspoof 2017 follows two special sessions on spoofing and countermeasures for automatic speaker verification at INTERSPEECH 2013 [13] and 2015 [14] which formed the first evaluation, ASVspoof 2015 [15].”
BackgroundSelf-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
Self-citationA New Feature for Automatic Speaker Verification Anti-Spoofing: Constant Q Cepstral Coefficients
Todisco, M., Delgado, H., & Evans, N. (2016) · The Speaker and Language Recognition Workshop (Odyssey) · doi:10.21437/odyssey.2016-41 · Cited by 340
Self-citationText-dependent speaker verification: Classifiers, databases and RSR2015
Larcher, A., Lee, K. A., Ma, B., & Li, H. (2014) · Speech Communication · doi:10.1016/j.specom.2014.03.001 · Cited by 239
Self-citation
Cited by
Sorted by citations
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Shi, Z., Li, C., Jin, Z., Sun, W., Ji, X., & Xu, W. (2021) · IEEE International Conference on Parallel and Distributed Systems (ICPADS) · doi:10.1109/icpads53394.2021.00027 · Cited by 6
Zhang, X., Zhang, X., Zou, X., Liu, H., & Sun, M. (2022) · Security and Communication Networks · doi:10.1155/2022/2666534 · Cited by 6
Voice Presentation Attack Detection Using Convolutional Neural Networks
Himawan, I., Madikeri, S. R., Motlíček, P., Cerňak, M., Sridharan, S., & Fookes, C. (2019) · Advances in computer vision and pattern recognition · doi:10.1007/978-3-319-92627-8_17 · Cited by 7
Retracted: Mimicry Voice Detection using Convolutional Neural Networks
Neelima, M., & SantiPrabha, I. (2020) · International Conference on Smart Electronics and Communication (ICOSEC) · doi:10.1109/icosec49089.2020.9215407 · Cited by 6
RetractedAFP-Conformer: Asymptotic feature pyramid conformer for spoofing speech detection
Huang, Y., Shen, Q., & Ma, J. (2024) · Speech Communication · doi:10.1016/j.specom.2024.103149 · Cited by 6
Multi-Channel Replay Speech Detection Using an Adaptive Learnable Beamformer
Neri, M., & Virtanen, T. I. (2025) · IEEE Open Journal of Signal Processing · doi:10.1109/ojsp.2025.3568758 · Cited by 5
Deepfake Audio Detection: A Comparative Study of Advanced Deep Learning Models
Verma, K., Mittal, D., Samanta, S., Gulati, K., Kulkarni, O., Dar, M. A., & Biji, C. L. (2025) · IEEE Access · doi:10.1109/access.2025.3611839 · Cited by 5
Neamtu, C.-T., Mihalache, Ş., & Burileanu, D. (2023) · International Conference on Speech Technology and Human-Computer Dialogue (SpeD) · doi:10.1109/sped59241.2023.10314940 · Cited by 5
Artificial speech detection using image-based features and random forest classifier
Tan, C. B., Hijazi, M. H. A., Kok, F., Mohamad, M. S., & Nohuddin, P. N. E. (2022) · IAES International Journal of Artificial Intelligence · doi:10.11591/ijai.v11.i1.pp161-172 · Cited by 5
Deepfake Detection System for the ADD Challenge Track 3.2 Based on Score Fusion
Zhang, Y., Lu, J., Wang, X., Li, Z., Xiao, R., Wang, W., Li, M., & Zhang, P. (2022) · doi:10.1145/3552466.3556528 · Cited by 6
Speaker and language recognition and characterization: Introduction to the CSL special issue
Lleida, E., & Rodríguez-Fuentes, L. J. (2017) · Computer Speech & Language · doi:10.1016/j.csl.2017.12.001 · Cited by 6
Hitting Three Birds with One System: A Voice-Based CAPTCHA for the Modern User
Shah, M. A., & Harras, K. A. (2018) · IEEE International Conference on Web Services (ICWS) · doi:10.1109/icws.2018.00040 · Cited by 4
Voice Liveness Detection for Medical Devices
Hao, B., & Hei, X. (2018) · Advances in medical technologies and clinical practice book series · doi:10.4018/978-1-5225-7525-2.ch005 · Cited by 5
Overview of methods for extracting acoustic speech features in speaker recognition
Sudjenkova, A. (2020) · Transaction of Scientific Papers of the Novosibirsk State Technical University · doi:10.17212/2307-6879-2019-3-4-139-164 · Cited by 5
Self-Supervised Pre-Training with Acoustic Configurations for Replay Spoofing Detection
Shim, H.-J., Heo, H.-S., Jung, J.-W., & Yu, H.-J. (2020) · Interspeech (USB) · doi:10.21437/interspeech.2020-1345 · Cited by 5
Listening to Sounds of Silence for Audio replay attack detection
Hajipour, M., Akhaee, M. A., & Toosi, R. (2021) · International Conference on Signal Processing and Intelligent Systems (ICSPIS) · doi:10.1109/icspis54653.2021.9729353 · Cited by 5
Guo, H., Yan, Q., Ivanov, N., Zhu, Y., Xiao, L., & Hunter, E. J. (2022) · ACM Asia Conference on Computer and Communications Security (ASIA CCS) · doi:10.1145/3488932.3517420 · Cited by 5
Altuwayjiri, S. M., Bchir, O., & Ismail, M. M. B. (2022) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2022.0130596 · Cited by 5
A Survey of Adversarial Attacks on Wireless Communications
Luo, X., Qin, Q., Gong, X., & Xue, M. (2022) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-04231-7_7 · Cited by 5
Improving Spoofing Capability for End-to-end Any-to-many Voice Conversion
Hua, H., Chen, Z., Zhang, Y., Li, M., & Zhang, P. (2022) · doi:10.1145/3552466.3556532 · Cited by 6
Gupta, P., Chodingala, P. K., & Patil, H. A. (2022) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco55093.2022.9909533 · Cited by 5
The Database and Benchmark For the Source Speaker Tracing Challenge 2024
Li, Z., Lin, Y., Tian, Y., Suo, H., Zhang, P., Ren, Y., Cai, Z., Nishizaki, H., & Li, M. (2024) · IEEE Spoken Language Technology Workshop (SLT) · doi:10.1109/slt61566.2024.10832292 · Cited by 5
Shaik Mohammad Rafi, B., & Murty, K. S. R. (2019) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2019.8683500 · Cited by 5
Multilingual Voice Impersonation Dataset and Evaluation
Mandalapu, H., Ramachandra, R., & Busch, C. (2021) · Communications in computer and information science · doi:10.1007/978-3-030-71711-7_15 · Cited by 5
An Audio Fingerprinting Approach to Replay Attack Detection on ASVSPOOF 2017 Challenge Data
González-Rodríguez, J., Escudero, A., de Benito-Gorrón, D., Labrador, B., & Franco-Pedroso, J. (2018) · The Speaker and Language Recognition Workshop (Odyssey) · doi:10.21437/odyssey.2018-43 · Cited by 4
Voice Conversion and Spoofed Voice Detection from Parallel English and Urdu Corpus using Cyclic GANs
Saleem, S., Dilawari, A., Ghani Khan, M. U., & Husnain, M. (2019) · 2019 International Conference on Robotics and Automation in Industry (ICRAI) · doi:10.1109/icrai47710.2019.8967385 · Cited by 5
Multimodal Speaker Recognition using voice and lip movement with decision and feature level fusionPreprint
Nainan, S., & Kulkarni, V. H. (2024) · Research Square · doi:10.21203/rs.3.rs-2960709/v1 · Cited by 5
A multi-level and iterative feature engineering framework for deepfake audio detection
Çelik, B., & Akbal, A. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.130722 · Cited by 5
Audio Deepfake Detection via a Fuzzy Dual-Path Time-Frequency Attention Network
Li, J., Wang, H., Xie, F., Feng, X., Chen, J., Liu, J., & Wang, J. (2025) · Sensors · doi:10.3390/s25247608 · Cited by 4
Capsule-based and TCN-based Approaches for Spoofing Detection in Voice Biometry
Borodin, K., Kudryavtsev, V., Mkrtchian, G., & Gorodnichev, M. G. (2024) · Engineering Technology & Applied Science Research · doi:10.48084/etasr.8906 · Cited by 5
Room Impulse Responses Help Attackers to Evade Deep Fake Detection
Luong, H.-T., Truong, D.-T., Lee, K. A., & Chng, E. S. (2024) · IEEE Spoken Language Technology Workshop (SLT) · doi:10.1109/slt61566.2024.10832177 · Cited by 6
Self-citationVPCID—A VoIP Phone Call Identification Database
Huang, Y., Tan, S., Li, B., & Huang, J. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-11389-6_23 · Cited by 4
de Almeida, A. M. G., Recco, C. H., & Guido, R. C. (2021) · Proceedings of the ... International Florida Artificial Intelligence Research Society Conference · doi:10.32473/flairs.v34i1.128370 · Cited by 4
A Hybrid Classification Approach For Artificial Speech Detection
Tan, C. B., Hijazi, M. H. A., & Nohuddin, P. N. E. (2023) · IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) · doi:10.1109/iicaiet59451.2023.10291764 · Cited by 4
A Review of User Identification Methods Based on Digital Fingerprint
Владимирович, О. А., & Murashko, Y. (2023) · Proceedings of Telecommunication Universities · doi:10.31854/1813-324x-2023-9-5-91-111 · Cited by 4
A Green Learning Approach to Spoofed Speech Detection
Wei, C., Pang, R., & Kuo, C. ‐. J. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10448336 · Cited by 5
Yanagi, Y., Orihara, R., Tahara, Y., Sei, Y., Alumäe, T., & Ohsuga, A. (2024) · Annals of Emerging Technologies in Computing · doi:10.33166/aetic.2024.02.002 · Cited by 4
Multi-Scale Information Aggregation for Spoofing Detection
Li, C., Wan, Y., Yang, F., & Yang, J. J. (2022) · EURASIP Journal on Audio Speech and Music Processing · doi:10.1186/s13636-024-00379-x · Cited by 7
GNCL: A Graph Neural Network with Consistency Loss for Segment-Level Spoofed Speech Detection
Ge, Z., Xu, X., Guo, H., Yang, Z., & Schuller, B. W. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10888281 · Cited by 4
Data augmentation and post selection for improved replay attack detection
Zhao, Y., Togneri, R. B., & Sreeram, V. (2019) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.1109/apsipaasc47483.2019.9023070 · Cited by 4
Kasapoglu, B., & Koç, T. (2020) · European Journal of Science and Technology · doi:10.31590/ejosat.780650 · Cited by 4
Practical Speech Re-use Prevention in Voice-driven Services
Zhang, Y., Arora, S. S., Shirvanian, M., Huang, J., & Gu, G. (2021) · International Symposium on Research in Attacks, Intrusions and Defenses (RAID) · doi:10.1145/3471621.3471855 · Cited by 4
BPCNN: Bi-Point Input for Convolutional Neural Networks in Speaker Spoofing Detection
Yoon, S.-H., & Yu, H.-J. (2022) · Sensors · doi:10.3390/s22124483 · Cited by 5
Light-weight Frequency Information Aware Neural Network Architecture for Voice Spoofing Detection
Choi, S., Oh, S., Yang, J., Lee, Y., & Kwak, I. (2022) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr56361.2022.9956079 · Cited by 4
Non-Cepstral Uncertainty Vector for Replay Spoofed Speech Detection
Patil, H. A., Acharya, R., Patil, A. T., & Gupta, P. (2022) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco55093.2022.9909946 · Cited by 4
Xu, X., Fu, C., Du, X., & Ratazzi, E. P. (2023) · International Conference on Dependable Systems & Networks/Proceedings · doi:10.1109/dsn58367.2023.00060 · Cited by 4
Improved One-class Learning for Voice Spoofing Detection
Li, L., Xue, X., Peng, H., Ren, Y., & Zhao, M. (2023) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.1109/apsipaasc58517.2023.10317117 · Cited by 4
Feature Fusion and Voiceprint-Based Access Control for Wireless Insulin Pump Systems
Ping, Y., Hao, B., Hei, X., Tu, Y., Du, X., & Wu, J. (2019) · IEEE Access · doi:10.1109/access.2019.2937805 · Cited by 4
Investigating Feature Reduction Strategies for Replay Antispoofing in Voice Biometrics
Mankad, S. H., Garg, S., Patel, M., & Adalja, H. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-34872-4_44 · Cited by 4
Preventing Fake Information Generation Against Media Clone Attacks
Babaguchi, N., Echizen, I., Yamagishi, J., Nitta, N., Nakashima, Y., Nakamura, K., Kono, K., Fang, F., Myojin, S., Kuang, Z., et al. (2020) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2020mui0001 · Cited by 4
Self-citationSignificance of Teager Energy Operator Phase for Replay Spoof Detection
Tapkir, P. A., & Patil, H. A. (2018) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.23919/apsipa.2018.8659664 · Cited by 4
Towards robust audio spoofing detection: a detailed comparison of traditional and learned featuresPreprint
Balamurali, B. T., Lin, K. W. E., Lui, S., Chen, J., & Herremans, D. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.12439 · Cited by 4
RW-Resnet: A Novel Speech Anti-Spoofing Model Using Raw WaveformPreprint
Ma, Y., Ren, Z., & Xu, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.05684 · Cited by 4
Advancing Continual Learning for Robust Deepfake Audio Classification
Dong, F., Tang, Q., Bai, Y., & Wang, Z. (2024) · TENCON 2024 - 2024 IEEE Region 10 Conference (TENCON) · doi:10.1109/tencon61640.2024.10902761 · Cited by 3
Detection of AI Generated Speech using Speech Recognition with MFCC and GMM
Sharma, A., Sharma, A., & Pant, U. (2024) · International Conference on Advances in Computing, Communication and Materials (ICACCM) · doi:10.1109/icaccm61117.2024.11059121 · Cited by 3
Review of AIGC passive detection and proactive protection
Liu, X., Liu, H., Zhao, Y., Ni, R., Li, X., & Guo, M. (2025) · Scientia Sinica Informationis · doi:10.1360/ssi-2025-0043 · Cited by 3
Novel Demodulation-Based Features using Classifier-level Fusion of GMM and CNN for Replay Detection
Kamble, M. R., Tak, H., Krishna, M. V. S., & Patil, H. A. (2018) · International Symposium on Chinese Spoken Language Processing (ISCSLP) · doi:10.1109/iscslp.2018.8706648 · Cited by 3
Wang, X., Yamagishi, J., Todisco, M., Delgado, H., Nautsch, A., Evans, N., Sahidullah, M., Vestman, V., Kinnunen, T., Lee, K. A., et al. (2019) · Graduate School and Research Center in Digital Science (EURECOM) · Cited by 3
Self-citationDiscriminative Feature Extraction Based on SWM for Playback Attack Detection
Li, W., & Xia, M. (2023) · IEEE World Conference on Applied Intelligence and Computing (AIC) · doi:10.1109/aic57670.2023.10263831 · Cited by 3
WaveSpect: A Hybrid Approach to Synthetic Audio Detection via Waveform and Spectrogram Analysis
Chen, D., Huang, F., Song, Z., Zhu, W., Yang, Y., & Zeng, K. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10890188 · Cited by 3
Wubet, Y. A., & Lian, K. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3577023 · Cited by 4
Development of playback attacks detection system
Singh, M., Mishra, J., & Pati, D. (2017) · IEEE Region 10 Conference (TENCON) · doi:10.1109/tencon.2017.8228080 · Cited by 3
Wu, Q., Xiong, S., & Zhu, Z. (2021) · IEEE Access · doi:10.1109/access.2021.3098058 · Cited by 3
Zeng, C., Zhang, L., Liu, M., & Yamagishi, J. (2022) · Interspeech (USB) · doi:10.21437/interspeech.2022-10495 · Cited by 3
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Detecting Replay Attacks Using Single-Channel Audio: The Temporal Autocorrelation of Speech
Lee, S.-K., Tsao, Y., & Wang, H. (2022) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.23919/apsipaasc55919.2022.9980046 · Cited by 3
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Galajit, K., Kosolsriwiwat, T., Unoki, M., Mawalim, C. O., Aimmanee, P., Kongprawechnon, W., Pa, W. P., Chaiwongyen, A., Racharak, T., Boonkla, S., et al. (2023) · International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP) · doi:10.1109/isai-nlp60301.2023.10354956 · Cited by 3
Speech Spoofing Detection Based on Graph Attention Networks with Spectral and Temporal Information
Zhang, P., Chen, Y., Li, M., Zhao, H., Zhang, J., Wang, F., & Wu, X. (2023) · ACM International Conference on Multimedia in Asia (MMAsia) · doi:10.1145/3595916.3626406 · Cited by 3
Voice Privacy Using Time-Scale and Pitch Modification
Singh, D. K., Prajapati, G. P., & Patil, H. A. (2024) · SN Computer Science · doi:10.1007/s42979-023-02549-8 · Cited by 5
MOS-FAD: Improving Fake Audio Detection Via Automatic Mean Opinion Score Prediction
Zhou, W., Yang, Z., Chu, C., Li, S., Dabre, R., Zhao, Y., & Kawahara, T. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10446041 · Cited by 3
Investigating Text-Independent Speaker Verification Systems Under Varied Data Conditions
Das, R. K., & Prasanna, S. R. M. (2019) · Circuits Systems and Signal Processing · doi:10.1007/s00034-019-01028-x · Cited by 3
Constant-Q Deep Coefficients for Playback Attack Detection
Yang, J., Xu, L., & Bo, R. E. N. (2020) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2019edl8115 · Cited by 3
Demonstrating User Authentication via Electrical Muscle Stimulation
Chen, Y., Yang, Z., Abbou, R., Lopes, P., Zhao, B. Y., & Zheng, H. (2021) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3411763.3451563 · Cited by 3
Voiceprint-Based Access Control for Wireless Insulin Pump Systems
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Mankad, S. H., Shah, V., & Garg, S. (2018) · IEEE Region 10 Conference (TENCON) · doi:10.1109/tencon.2018.8650091 · Cited by 3
Wang, Y., Wang, X., Nishizaki, H., & Li, M. (2022) · International Symposium on Chinese Spoken Language Processing (ISCSLP) · doi:10.1109/iscslp57327.2022.10038240 · Cited by 3
Adversarial Defense for Automatic Speaker Verification by Self-Supervised LearningPreprint
Wu, H., Li, X., Liu, A. T., Wu, Z., Meng, H. M. L., & Lee, H.-Y. (2021) · arXiv (Cornell University) · Cited by 3
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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
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Issa, E. (2026) · Computation · doi:10.3390/computation14010020 · Cited by 3
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Speech Replay Spoofing Attack Detection System Based on Fusion of Classification Algorithms
Лепендин, А. А., Filin, Y. A., & Владимирович, М. П. (2018) · Izvestiya of Altai State University · doi:10.14258/izvasu(2018)1-19 · Cited by 2
Kasapoglu, B., & Koç, T. (2019) · National Conference on Electrical and Electronics Engineering (ELECO) · doi:10.23919/eleco47770.2019.8990465 · Cited by 2
Improved Processing of LP-residual Information for Detection of Replay Signals
Dutta, K., Singh, M., & Pati, D. (2019) · IEEE India Conference · doi:10.1109/indicon47234.2019.9030321 · Cited by 2
Deep Spectro-temporal Artifacts for Detecting Synthesized Speech
Liu, X., Liu, M., Zhang, L., Zhang, L., Zeng, C., Li, K., Li, N., Lee, K. A., Wang, L., & Dang, J. (2022) · doi:10.1145/3552466.3556527 · Cited by 4
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Metadata
- Authors
- Kinnunen, T., Sahidullah, M., Delgado, H., Todisco, M., Evans, N., Yamagishi, J., & Lee, K. A.
- Affiliations
- University of Eastern Finland (FI)
- EURECOM (FR)
- National Institute of Informatics (JP)
- Institute for Infocomm Research (SG)
- ORCID
- Venue
- Interspeech 2017
- Publisher
- ISCA
- Published
- 2017-08-20
- Pages
- 2-6
- ISSN
- 2308-457X (ISSN-L)
- OpenAlex
- W2745896134
- Cited by
- 535
- References
- 30
- Citations by year
- 2026: 31 · 2025: 50 · 2024: 67 · 2023: 61 · 2022: 76 · 2021: 54 · 2020: 71 · 2019: 72 · 2018: 40 · 2017: 13
- FWCI
- 24.45
- Type
- conference paper
- Language
- en
- Open access
- green
- erepo.uef.fi
- Licence
- other-oa
- Locations
- Interspeech (USB) (publishedVersion)
- UEF eRepo (University of Eastern Finland) (submittedVersion, other-oa)
- Graduate School and Research Center in Digital Science (EURECOM) (submittedVersion)
- Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, other-oa)
- Funders
- European Commission (647850, HORIZON2020)
- Ministry of Education, Culture, Sports, Science and Technology (16H06302, 15H01686, 26280066)
- Japan Society for the Promotion of Science (15H01686, 16H06302, 26280066)
- 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, spoofing attacks, equal error rate
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