A survey on presentation attack detection for automatic speaker verification systems: State-of-the-art, taxonomy, issues and future direction
Open accessAbstract
The emergence of biometric technology provides enhanced security compared to the traditional identification and authentication techniques that were less efficient and secure. Despite the advantages brought by biometric technology, the existing biometric systems such as Automatic Speaker Verification (ASV) systems are weak against presentation attacks. A presentation attack is a spoofing attack launched to subvert an ASV system to gain access to the system. Though numerous Presentation Attack Detection (PAD) systems were reported in the literature, a systematic survey that describes the current state of research and application is unavailable. This paper presents a systematic analysis of the state-of-the-art voice PAD systems to promote further advancement in this area. The objectives of this paper are two folds: (i) to understand the nature of recent work on PAD systems, and (ii) to identify areas that require additional research. From the survey, a taxonomy of voice PAD and the trend analysis of recent work on PAD systems were built and presented, whereby the recent and relevant articles including articles from Interspeech and ICASSP Conferences, mostly indexed by Scopus, published between 2015 and 2021 were considered. A total of 172 articles were surveyed in this work. The findings of this survey present the limitation of recent works, which include spoof-type dependent PAD. Consequently, the future direction of work on voice PAD for interested researchers is established. The findings of this survey present the limitation of recent works, which include spoof-type dependent PAD. Consequently, the future direction of work on voice PAD for interested researchers is established.
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
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.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
1 retracted
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 · see citing works
RetractedFront-End Factor Analysis for Speaker Verification
Dehak, N., Kenny, P. J., Dehak, R., Dumouchel, P., & Ouellet, P. (2011) · IEEE Transactions on Audio, Speech, and Language Processing · doi:10.1109/tasl.2010.2064307 · Cited by 3,549 · see citing works
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 · see citing works
X-Vectors: Robust DNN Embeddings for Speaker Recognition
Snyder, D., Garcia-Romero, D., Sell, G., Povey, D., & Khudanpur, S. P. (2018) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2018.8461375 · Cited by 2,698 · see citing works
Reynolds, D. A. (2009) · Encyclopedia of Biometrics · doi:10.1007/978-0-387-73003-5_196 · Cited by 2,062 · see citing works
Speaker identification and verification using Gaussian mixture speaker models
Reynolds, D. A. (1995) · Speech Communication · doi:10.1016/0167-6393(95)00009-d · Cited by 1,216 · see citing works
Ross, A. A., Nandakumar, K., & Jain, A. K. (2006) · Kluwer Academic Publishers eBooks · doi:10.1007/0-387-33123-9 · Cited by 1,191 · see citing works
Deep Neural Network Embeddings for Text-Independent Speaker Verification
Snyder, D., Garcia-Romero, D., Povey, D., & Khudanpur, S. P. (2017) · Interspeech (USB) · doi:10.21437/interspeech.2017-620 · Cited by 759 · see citing works
Score Normalization for Text-Independent Speaker Verification Systems
Auckenthaler, R., Carey, M. J., & Lloyd-Thomas, H. (2000) · Digital Signal Processing · doi:10.1006/dspr.1999.0360 · Cited by 698 · see citing works
ASVspoof 2019: Future Horizons in Spoofed and Fake Audio Detection
Todisco, M., Wang, X., Vestman, V., Sahidullah, M., Delgado, H., Nautsch, A., Yamagishi, J., Evans, N., Kinnunen, T. H., & Lee, K. A. (2019) · Interspeech 2019 · doi:10.21437/interspeech.2019-2249 · Cited by 687 · see citing works
Bag of visual words and fusion methods for action recognition: Comprehensive study and good practice
Peng, X., Wang, L., Wang, X., & Qiao, Y. (2016) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2016.03.013 · Cited by 681 · see citing works
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 · see citing works
The ASVspoof 2017 challenge: assessing the limits of replay spoofing attack detection
Kinnunen, T., Sahidullah, M., Delgado, H., Todisco, M., Evans, N., Yamagishi, J., & Lee, K. A. (2017) · Interspeech 2017 · doi:10.21437/interspeech.2017-1111 · Cited by 535 · see citing works
End-to-end text-dependent speaker verification
Heigold, G., Moreno, I. L., Samy Bengio, A., & Shazeer, N. (2016) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2016.7472652 · Cited by 531 · see citing works
A new method of feature fusion and its application in image recognition
Sun, Q., Zeng, S.-G., Liu, Y., Heng, P., & De-shen, X. (2005) · Pattern Recognition · doi:10.1016/j.patcog.2004.12.013 · Cited by 517 · see citing works
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 · see citing works
ASVspoof 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 · see citing works
Li, S. Z., & Jain, A. K. (2015) · Springer eBooks · doi:10.1007/978-1-4899-7488-4 · Cited by 480 · see citing works
Feature fusion: parallel strategy vs. serial strategy
Yang, J. J., Yang, J. Y., Zhang, D., & Lu, J.-F. (2003) · Pattern Recognition · doi:10.1016/s0031-3203(02)00262-5 · Cited by 456 · see citing works
LPCNET: Improving Neural Speech Synthesis through Linear Prediction
Valin, J.-M., & Skoglund, J. (2019) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2019.8682804 · Cited by 427 · see citing works
Constant 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 · see citing works
A comparison of features for synthetic speech detection
Sahidullah, M., Kinnunen, T., & Hanilçi, C. (2015) · Interspeech 2015 · doi:10.21437/interspeech.2015-472 · Cited by 382 · see citing works
A 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 · see citing works
Audio replay attack detection with deep learning frameworks
Lavrentyeva, G., Novoselov, S., Malykh, E., Kozlov, A., Kudashev, O., & Shchemelinin, V. (2017) · Interspeech 2017 · doi:10.21437/interspeech.2017-360 · Cited by 292 · see citing works
Handbook of Biometric Anti-Spoofing
Anjos, A., Komulainen, J., Marcel, S., Hadid, A., Matti, P., Marcel, S., Nixon, M., & Z.Li, S. (2014) · Advances in computer vision and pattern recognition · doi:10.1007/978-1-4471-6524-8 · Cited by 271 · see citing works
Chingovska, I., Anjos, A., & Marcel, S. (2009) · Springer eBooks · doi:10.1007/978-3-642-27733-7 · Cited by 246 · see citing works
ASVspoof: The Automatic Speaker Verification Spoofing and Countermeasures Challenge
Wu, Z., Yamagishi, J., Kinnunen, T., Hanilci, C., Sahidullah, M., Sizov, A., Evans, N., Todisco, M., & Delgado, H. (2017) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2017.2671435 · Cited by 229 · see citing works
Mehta, N. A., Pandit, A., & Shukla, S. (2019) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2019.103311 · Cited by 207 · see citing works
Kinnunen, T., Lee, K. A., Delgado, H., Evans, N., Todisco, M., Sahidullah, M., Yamagishi, J., & Reynolds, D. A. (2018) · The Speaker and Language Recognition Workshop (Odyssey 2018) · doi:10.21437/odyssey.2018-44 · Cited by 198 · see citing works
ASVspoof 2017 Version 2.0: meta-data analysis and baseline enhancements
Delgado, H., Todisco, M., Sahidullah, M., Evans, N., Kinnunen, T., Lee, K. A., & Yamagishi, J. (2018) · The Speaker and Language Recognition Workshop (Odyssey 2018) · doi:10.21437/odyssey.2018-42 · Cited by 178 · see citing works
Patel, T. B., & Patil, H. A. (2015) · Interspeech (USB) · doi:10.21437/interspeech.2015-467 · Cited by 167 · see citing works
ASSERT: Anti-Spoofing with Squeeze-Excitation and Residual Networks
Lai, C.-I., Chen, N., Villalba, J., & Dehak, N. (2019) · Interspeech (USB) · doi:10.21437/interspeech.2019-1794 · Cited by 161 · see citing works
ResNet and Model Fusion for Automatic Spoofing Detection
Chen, Z., Xie, Z., Zhang, W., & Xu, X. (2017) · Interspeech (USB) · doi:10.21437/interspeech.2017-1085 · Cited by 155 · see citing works
Edge Computing with Cloud for Voice Disorder Assessment and Treatment
Muhammad, G., Alhamid, M. F., Alsulaiman, M., & Gupta, B. B. (2018) · IEEE Communications Magazine · doi:10.1109/mcom.2018.1700790 · Cited by 137 · see citing works
The reddots data collection for speaker recognition
Lee, K. A., Larcher, A., Wang, G., Kenny, P., Brümmer, N., Leeuwen, D. V., Aronowitz, H., Kockmann, M., Vaquero, C., Ma, B., et al. (2015) · Interspeech 2015 · doi:10.21437/interspeech.2015-95 · Cited by 136 · see citing works
Wu, Z., Das, R. K., Yang, J., & Li, H. (2020) · Interspeech (USB) · doi:10.21437/interspeech.2020-1810 · Cited by 115 · see citing works
Replay Attack Detection Using DNN for Channel Discrimination
Nagarsheth, P., Khoury, E., Patil, K., & Garland, M. (2017) · Interspeech (USB) · doi:10.21437/interspeech.2017-1377 · Cited by 110 · see citing works
Analysis of Score Normalization in Multilingual Speaker Recognition
Matějka, P., Novotný, O., Plchot, O., Burget, L., Sánchez, M. D., & Černocký, J. (2017) · Interspeech (USB) · doi:10.21437/interspeech.2017-803 · Cited by 109 · see citing works
Kinnunen, T., Sahidullah, M., Falcone, M., Costantini, L., Hautamäki, R. G., Thomsen, D. A. L., Sarkar, A. K., Tan, Z., Delgado, H., Todisco, M., et al. (2017) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2017.7953187 · Cited by 107 · see citing works
Advances in anti-spoofing: from the perspective of ASVspoof challenges
Kamble, M. R., Sailor, H. B., Patil, H. A., & Li, H. (2020) · APSIPA Transactions on Signal and Information Processing · doi:10.1017/atsip.2019.21 · Cited by 102 · see citing works
Toward a Universal Synthetic Speech Spoofing Detection Using Phase Information
Sánchez, J., Saratxaga, I., Hernáez, I., Navas, E., Erro, D., & Raitio, T. (2015) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2015.2398812 · Cited by 88 · see citing works
Yamagishi, J., Massimiliano, T., Sahidullah, M., Delgado, H., Wang, X., Evans, N., Kinnunen, T., Lee, K. A., Vestman, V., & Nautsch, A. (2015) · UEF eRepo (University of Eastern Finland) · doi:10.7488/ds/298 · Cited by 85 · see citing works
Relative phase information for detecting human speech and spoofed speech
Wang, L., Yoshida, Y., Kawakami, Y., & Nakagawa, S. (2015) · Interspeech (USB) · doi:10.21437/interspeech.2015-473 · Cited by 80 · see citing works
Abozaid, A., Haggag, A., Kasban, H., & Eltokhy, M. A. R. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-018-7012-3 · Cited by 79 · see citing works
Spectral Features for Synthetic Speech Detection
Paul, D., Pal, M., & Saha, G. (2017) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2017.2684705 · Cited by 77 · see citing works
Deep Speaker Feature Learning for Text-Independent Speaker Verification
Li, L., Chen, Y., Shi, Y., Tang, Z., & Wang, D. (2017) · Interspeech (USB) · doi:10.21437/interspeech.2017-452 · Cited by 73 · see citing works
Spoofing detection with DNN and one-class SVM for the ASVspoof 2015 challenge
Villalba, J., Miguel, A., Ortega, A., & Lleida, E. (2015) · Interspeech 2015 · doi:10.21437/interspeech.2015-468 · Cited by 72 · see citing works
Introduction to Voice Presentation Attack Detection and Recent Advances
Sahidullah, M., Delgado, H., Todisco, M., Kinnunen, T., Evans, N., Yamagishi, J., & Lee, K. A. (2019) · Advances in computer vision and pattern recognition · doi:10.1007/978-3-319-92627-8_15 · Cited by 70 · see citing works
End-to-end spoofing detection with raw waveform CLDNNS
Dinkel, H., Chen, N., Qian, Y., & Yu, K. (2017) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2017.7953080 · Cited by 69 · see citing works
Cai, W., Cai, D., Liu, W., Li, G., & Li, M. (2017) · Interspeech (USB) · doi:10.21437/interspeech.2017-906 · Cited by 67 · see citing works
Generalized end-to-end detection of spoofing attacks to automatic speaker recognizers
Monteiro, J., Alam, J., & Falk, T. H. (2020) · Computer Speech & Language · doi:10.1016/j.csl.2020.101096 · Cited by 64 · see citing works
Detecting Spoofing Attacks Using VGG and SincNet: BUT-Omilia Submission to ASVspoof 2019 Challenge
Zeinali, H., Stafylakis, T., Athanasopoulou, G., Rohdin, J., Gkinis, I., Burget, L., & Černocký, J. (2019) · Interspeech (USB) · doi:10.21437/interspeech.2019-2892 · Cited by 63 · see citing works
A Light-Weight Replay Detection Framework For Voice Controlled IoT Devices
Malik, K. M., Javed, A., Malik, H. M. A., & Irtaza, A. (2020) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2020.2999828 · Cited by 63 · see citing works
Synthetic speech detection using fundamental frequency variation and spectral features
Pal, M., Paul, D., & Saha, G. (2017) · Computer Speech & Language · doi:10.1016/j.csl.2017.10.001 · Cited by 58 · see citing works
Classifiers for synthetic speech detection: a comparison
Hanilçi, C., Kinnunen, T., Sahidullah, M., & Сизов, А. С. (2015) · Interspeech (USB) · doi:10.21437/interspeech.2015-466 · Cited by 57 · see citing works
Deepfake Audio Detection: A Deep Learning Based Solution for Group Conversations
Wijethunga, R. L. M. A. P. C., Matheesha, D. M. K., Noman, A. A., De Silva, K. H. V. T. A., Tissera, M., & Rupasinghe, L. (2020) · 2020 2nd International Conference on Advancements in Computing (ICAC) · doi:10.1109/icac51239.2020.9357161 · Cited by 57 · see citing works
End-to-End convolutional neural network-based voice presentation attack detection
Muckenhirn, H., Magimai.-Doss, M., & Marcel, S. (2017) · IEEE International Joint Conference on Biometrics (IJCB) · doi:10.1109/btas.2017.8272715 · Cited by 55 · see citing works
Dua, M., Jain, C., & Kumar, S. (2021) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-021-02960-0 · Cited by 54 · see citing works
Extended Constant-Q Cepstral Coefficients for Detection of Spoofing Attacks
Yang, J., Das, R. K., & Li, H. (2018) · 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.23919/apsipa.2018.8659537 · Cited by 53 · see citing works
Long Range Acoustic Features for Spoofed Speech Detection
Das, R. K., Yang, J., & Li, H. (2019) · Interspeech 2019 · doi:10.21437/interspeech.2019-1887 · Cited by 53 · see citing works
Speaker Recognition Anti-spoofing
Evans, N., Kinnunen, T., Yamagishi, J., Wu, Z., Alegre, F., & Leon, P. L. D. (2014) · Advances in computer vision and pattern recognition · doi:10.1007/978-1-4471-6524-8_7 · Cited by 51 · see citing works
A Survey on Replay Attack Detection for Automatic Speaker Verification (ASV) System
Patil, H. A., & Kamble, M. R. (2018) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.23919/apsipa.2018.8659666 · Cited by 50 · see citing works
Lei, Z., Yang, Y., Liu, C., & Ye, J. (2020) · Interspeech (USB) · doi:10.21437/interspeech.2020-2723 · Cited by 50 · see citing works
Synthetic speech detection using phase information
Saratxaga, I., Sánchez, J., Wu, Z., Hernáez, I., & Navas, E. (2016) · Speech Communication · doi:10.1016/j.specom.2016.04.001 · Cited by 47 · see citing works
On Joint Optimization of Automatic Speaker Verification and Anti-Spoofing in the Embedding Space
Gomez-Alanis, A., González, J. A., Dubagunta, S. P., Peinado, A. M., & Magimai.-Doss, M. (2020) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2020.3039045 · Cited by 45 · see citing works
On the study of replay and voice conversion attacks to text-dependent speaker verification
Wu, Z., & Li, H. (2015) · Multimedia Tools and Applications · doi:10.1007/s11042-015-3080-9 · Cited by 43 · see citing works
ReMASC: Realistic Replay Attack Corpus for Voice Controlled Systems
Gong, Y., Yang, J., Huber, J., MacKnight, M., & Poellabauer, C. (2019) · Interspeech (USB) · doi:10.21437/interspeech.2019-1541 · Cited by 39 · see citing works
Generalization of spoofing countermeasures: A case study with ASVspoof 2015 and BTAS 2016 corpora
Paul, D., Sahidullah, M., & Saha, G. (2017) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2017.7952516 · Cited by 38 · see citing works
Recurrent Neural Networks for Automatic Replay Spoofing Attack Detection
Chen, Z., Zhang, W., Xie, Z., Xu, X., & Chen, D. (2018) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2018.8462644 · Cited by 38 · see citing works
Voice spoofing detection corpus for single and multi-order audio replays
Baumann, R., Malik, K. M., Javed, A., Ball, A., Kujawa, B., & Malik, H. (2021) · Computer Speech & Language · doi:10.1016/j.csl.2020.101132 · Cited by 36 · see citing works
McGettigan, C., Eisner, F., Agnew, Z. K., Manly, T., Wisbey, D., & Scott, S. K. (2013) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_00427 · Cited by 35 · see citing works
Korshunov, P., & Marcel, S. (2017) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2017.2692389 · Cited by 35 · see citing works
Analysing The Predictions Of a CNN-Based Replay Spoofing Detection System
Chettri, B., Mishra, S., Sturm, B. L. T., & Benetos, E. (2018) · IEEE Spoken Language Technology Workshop (SLT) · doi:10.1109/slt.2018.8639666 · Cited by 35 · see citing works
Dataset artefacts in anti-spoofing systems: A case study on the ASVspoof 2017 benchmark
Chettri, B., Benetos, E., & Sturm, B. L. T. (2020) · IEEE/ACM Transactions on Audio, Speech, and Language Processing · doi:10.1109/taslp.2020.3036777 · Cited by 35 · see citing works
Detecting Replay Attacks Using Multi-Channel Audio: A Neural Network-Based Method
Gong, Y., Yang, J., & Poellabauer, C. (2020) · IEEE Signal Processing Letters · doi:10.1109/lsp.2020.2996908 · Cited by 34 · see citing works
A Kernel Density Estimation Based Loss Function and its Application to ASV-Spoofing Detection
Gomez-Alanis, A., González, J. A., & Peinado, A. M. (2020) · IEEE Access · doi:10.1109/access.2020.3000641 · Cited by 34 · see citing works
On robustness of speech based biometric systems against voice conversion attack
Pal, M., & Saha, G. (2015) · Applied Soft Computing · doi:10.1016/j.asoc.2015.01.036 · Cited by 32 · see citing works
Mallouh, A. A., Qawaqneh, Z., & Barkana, B. D. (2017) · Neural Computing and Applications · doi:10.1007/s00521-017-2848-4 · Cited by 32 · see citing works
Cross-Database Evaluation of Audio-Based Spoofing Detection Systems
Korshunov, P., & Marcel, S. (2016) · Interspeech (USB) · doi:10.21437/interspeech.2016-1326 · Cited by 31 · see citing works
Li, J., Sun, M., Zhang, X., & Wang, Y. (2020) · IEEE Access · doi:10.1109/access.2020.2964048 · Cited by 31 · see citing works
An Evaluation of Score Level Fusion Approaches for Fingerprint and Finger-vein BiometricsPreprint
Vishi, K., & Mavroeidis, V. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.10666 · Cited by 31 · see citing works
A Deep Identity Representation for Noise Robust Spoofing Detection
Gomez-Alanis, A., Peinado, A. M., González, J. A., & Gomez, A. M. (2018) · Interspeech (USB) · doi:10.21437/interspeech.2018-1909 · Cited by 27 · see citing works
Replay spoofing countermeasure using autoencoder and siamese networks on ASVspoof 2019 challenge
Adiban, M., Sameti, H., & Shehnepoor, S. (2020) · Computer Speech & Language · doi:10.1016/j.csl.2020.101105 · Cited by 27 · see citing works
Can a Professional Imitator Fool a GMM-Based Speaker Verification System?
Mariéthoz, J., & Samy Bengio, A. (2005) · Cited by 27 · see citing works
Text Dependent Speaker Verification Using Un-Supervised HMM-UBM and Temporal GMM-UBM
Sarkar, A. K., & Tan, Z. (2016) · Interspeech (USB) · doi:10.21437/interspeech.2016-362 · Cited by 25 · see citing works
A Deeper Look at Gaussian Mixture Model Based Anti-Spoofing Systems
Chettri, B., & Sturm, B. L. T. (2018) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2018.8461467 · Cited by 23 · see citing works
Investigating Light-ResNet Architecture for Spoofing Detection Under Mismatched Conditions
Parasu, P., Epps, J., Sriskandaraja, K., & Suthokumar, G. (2020) · Interspeech (USB) · doi:10.21437/interspeech.2020-2039 · Cited by 21 · see citing works
A Performance Comparison of Statistical and Machine Learning Techniques in Learning Time Series Data
Haviluddin, H., Alfred, R., Obit, J. H., Hijazi, M. H. A., & Ibrahim, A. A. A. (2015) · Advanced Science Letters · doi:10.1166/asl.2015.6490 · Cited by 21 · see citing works
Self-citationData selection for i-vector based automatic speaker verification anti-spoofing
Hanilçi, C. (2017) · Digital Signal Processing · doi:10.1016/j.dsp.2017.10.010 · Cited by 19 · see citing works
Voice conversion spoofing detection by exploring artifacts estimates
Hemavathi, R., & Kumaraswamy, R. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-020-10212-0 · Cited by 19 · see citing works
Deep domain adaptation for anti-spoofing in speaker verification systems
Himawan, I., Villavicencio, F., Sridharan, S., & Fookes, C. (2019) · Computer Speech & Language · doi:10.1016/j.csl.2019.05.007 · Cited by 17 · see citing works
Monteiro, J., Alam, J., & Falk, T. H. (2020) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp40776.2020.9054558 · Cited by 17 · see citing works
Densely Connected Convolutional Network for Audio Spoofing Detection
Wang, Z., Cui, S., Kang, X., Sun, W., & Li, Z. (2020) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · Cited by 17 · see citing works
DNN Based Speaker Embedding Using Content Information for Text-Dependent Speaker Verification
Dey, S., Koshinaka, T., Motlíček, P., & Madikeri, S. R. (2018) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2018.8461389 · Cited by 17 · see citing works
Demiroğlu, C., Büyük, O., Khodabakhsh, A., & Maia, R. (2017) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2017.2673807 · Cited by 16 · see citing works
On the Generalization of Fused Systems in Voice Presentation Attack Detection
Gonçalves, A. R., Violato, R. P. V., Korshunov, P., Marcel, S., & Simões, F. O. (2017) · International Conference of the Biometrics Special Interest Group (BIOSIG) · doi:10.23919/biosig.2017.8053516 · Cited by 15 · see citing works
Features and classifiers for replay spoofing attack detection
Hanilçi, C. (2017) · Bursa Technical University Institutional Repository · Cited by 14 · see citing works
Combining evidences from Hilbert envelope and residual phase for detecting replay attacks
Singh, M., & Pati, D. (2019) · International Journal of Speech Technology · doi:10.1007/s10772-019-09604-x · Cited by 14 · see citing works
Processing linear prediction residual signal to counter replay attacks
Mishra, J., Singh, M., & Pati, D. (2018) · International Conference on Signal Processing and Communications (SPCOM) · doi:10.1109/spcom.2018.8724390 · Cited by 14 · see citing works
A Review On Speaker Recognition Approaches And Challenges
Sharma, V., & Bansal, P. (2013) · Cited by 14 · see citing works
Deep speaker verification: Do we need end to end?
Wang, D., Li, L., Tang, Z., & Zheng, T. F. (2017) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.1109/apsipa.2017.8282024 · Cited by 14 · see citing works
Sarria-Paja, M., Senoussaoui, M. E. A., O’Shaughnessy, D. D., & Falk, T. H. (2016) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2016.7472725 · Cited by 13 · see citing works
Sailor, H. B., Kamble, M. R., & Patil, H. A. (2017) · Interspeech (USB) · doi:10.21437/interspeech.2017-1393 · Cited by 12 · see citing works
Speech frame selection for spoofing detection with an application to partially spoofed audio-data
Kumar, A. K., Paul, D., Pal, M., Sahidullah, M., & Saha, G. (2021) · International Journal of Speech Technology · doi:10.1007/s10772-020-09785-w · Cited by 12 · see citing works
A Review on Speaker Recognition
Sreedharan, S., & Dr.Chandra, E. (2017) · International Journal of Engineering and Technology · doi:10.21817/ijet/2017/v9i3/170903513 · Cited by 12 · see citing works
Singh, R., Jiménez, A., & Øland, A. (2017) · IET Biometrics · doi:10.1049/iet-bmt.2016.0126 · Cited by 11 · see citing works
Al-Ali, A. K. H., Senadji, B., & Naik, G. R. (2017) · IEEE International Conference on Signal and Image Processing Applications (ICSIPA) · doi:10.1109/icsipa.2017.8120601 · Cited by 11 · see citing works
An improved GMM-based clustering algorithm for efficient speaker identification
Lin, W. (2015) · International Conference on Computer Science and Network Technology (ICCSNT) · doi:10.1109/iccsnt.2015.7491011 · Cited by 10 · see citing works
Challenges of integrating smart home automation with cloud based voice recognition systems
Matic, M., Stefanovic, I., Radosavac, U., & Vidaković, M. (2017) · IEEE International Conference on Consumer Electronics - Berlin (ICCE-Berlin) · doi:10.1109/icce-berlin.2017.8210640 · Cited by 9 · see citing works
Learning to boost GMM based speaker verification
Li, S. Z., Zhang, D., Ma, C., Shum, H., & Chang, E. (2003) · European Conference on Speech Communication and Technology (Eurospeech) · doi:10.21437/eurospeech.2003-480 · Cited by 9 · see citing works
A Novel Approach Towards Generalization of Countermeasure for Spoofing Attack on ASV Systems
Kumar, S. R., & Bharathi, B. (2020) · Circuits Systems and Signal Processing · doi:10.1007/s00034-020-01501-y · Cited by 9 · see citing works
Speaker verification anti-spoofing using linear prediction residual phase features
Hanilçi, C. (2017) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco.2017.8081176 · Cited by 8 · see citing works
Adversarial Multi-Task Learning for Speaker Normalization in Replay Detection
Suthokumar, G., Sethu, V., Sriskandaraja, K., & Eliathamby Ambikairajah, A. (2020) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp40776.2020.9054322 · Cited by 8 · see citing works
Detection of Replay Attack Based on Normalized Constant Q Cepstral Feature
Ye, Y., Lao, L., Yan, D., & Lin, L. (2019) · International Conference on Cloud Computing and Big Data Analytics (ICCCBDA) · doi:10.1109/icccbda.2019.8725688 · Cited by 7 · see citing works
Noise robust speaker verification using GMM-UBM multi-condition training
Mekonnen, B. W., & Dufera, B. D. (2015) · IEEE AFRICON · doi:10.1109/afrcon.2015.7331916 · Cited by 7 · see citing works
Safavi, S., Gan, H., & Mporas, I. (2017) · IEEE International Colloquium on Signal Processing & Its Applications (CSPA) · doi:10.1109/cspa.2017.8064954 · Cited by 6 · see citing works
Deep Neural Network Based Discriminative Training for I-Vector/PLDA Speaker Verification
Zheng, T., Jiqing, H., & Guibin, Z. (2018) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2018.8461344 · Cited by 6 · see citing works
Energy Separation Based Features for Replay Spoof Detection for Voice Assistant
Prajapati, G. P., Kamble, M. R., & Patil, H. A. (2020) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco47968.2020.9287577 · Cited by 6 · see citing works
Speaker Verification and Identification
Jin, M., & Yoo, C. D. (2010) · IGI Global eBooks · doi:10.4018/978-1-60566-725-6.ch013 · Cited by 5 · see citing works
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 · see citing works
Malware Classification Using Ensemble Classifiers
Hijazi, M. H. A., Beng, T. C., Mountstephens, J., Lim, Y., & Nisar, K. (2018) · Advanced Science Letters · doi:10.1166/asl.2018.10710 · Cited by 5 · see citing works
Self-citationSpeech spoofing detection using SVM and ELM technique with acoustic features
Rahmeni, R., Aicha, A. B., & BenAyed, Y. (2020) · International Conference on Advanced Technologies for Signal and Image Processing · doi:10.1109/atsip49331.2020.9231799 · Cited by 5 · see citing works
Subband Channel Selection using TEO for Replay Spoof Detection in Voice Assistants
Kotta, H., Patil, A. T., Acharya, R., & Patil, H. A. (2020) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · Cited by 4 · see citing works
Integrating online i-vector into GMM-UBM for text-dependent speaker verification
Jiang, X., Wang, S., Xiang, X., & Qian, Y. (2017) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.1109/apsipa.2017.8282293 · Cited by 4 · see citing works
Digital Forensics and Watermarking
(2020) · Lecture notes in computer science · doi:10.1007/978-3-030-43575-2 · Cited by 4 · see citing works
An Investigation about the Scalability of the Spoofing Detection System
Suthokumar, G., Sriskandaraja, K., Sethu, V., Wijenayake, C. A., & Eliathamby Ambikairajah, A. (2018) · International Conference on Information and Automation for Sustainability (ICIAfS) · doi:10.1109/iciafs.2018.8913369 · Cited by 3 · see citing works
GRU-SVM Model for Synthetic Speech Detection
Huang, T., Wang, H., Chen, Y., & He, P. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-43575-2_9 · Cited by 3 · see citing works
Adel, M., Afify, M., Gaballah, A., & Fayek, M. B. (2018) · IEEE Spoken Language Technology Workshop (SLT) · doi:10.1109/slt.2018.8639574 · Cited by 3 · see citing works
Voice Antispoofing System Vulnerabilities Research
Sinitca, A., Efimchik, N. V., Shalugin, E. D., Toropov, V. A., & Simonchik, K. (2020) · IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering · doi:10.1109/eiconrus49466.2020.9039393 · Cited by 3 · see citing works
Presentation attack detection in voice biometrics
Korshunov, P., & Marcel, S. (2017) · Institution of Engineering and Technology eBooks · doi:10.1049/pbse004e_ch10 · Cited by 2 · see citing works
Novel Energy Separation Based Frequency Modulation Features for Spoofed Speech Classification
Kamble, M. R., & Patil, H. A. (2017) · International Conference on Advances in Pattern Recognition · doi:10.1109/icapr.2017.8593041 · Cited by 2 · see citing works
Bonifaco, H., Guzman, K. R., Jara, J. N., Jasareno, A. D., Zabala, A. C., Prado, S. V., & Buenaventura, C. V. S. (2017) · IEEE International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM) · doi:10.1109/hnicem.2017.8269507 · Cited by 2 · see citing works
Speaker identification for personalized smart TVs
Tsai, W.-H., Lin, J.-C., Ma, C.-H., & Liao, Y. (2016) · IEEE International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan) · doi:10.1109/icce-tw.2016.7521051 · Cited by 2 · see citing works
From Scotland Yard to touchless authentication – fingerprinting makes its mark
Mather, F. (2017) · Biometric Technology Today · doi:10.1016/s0969-4765(17)30055-3 · Cited by 2 · see citing works
Wu, Z., & Kinnunen, T. (2015) · Cited by 1 · see citing works
Improving Replay Detection System with Channel Consistency DenseNeXt for the ASVspoof 2019 Challenge
Zhang, C., Cheng, J., Gu, Y., Wang, H., Jun, M., Wang, S., & Xiao, J. (2020) · Interspeech (USB) · doi:10.21437/interspeech.2020-1044 · Cited by 1 · see citing works
A new speaker verification algorithm based on identification results
Billal, K., & Dahimene, A. (2017) · International Conference on Electrical Engineering - Boumerdes (ICEE-B) · doi:10.1109/icee-b.2017.8192139 · Cited by 1 · see citing works
Flagged first, then by citations
Research works printed but not linked: 4
- Admuthe SS, Ghugardare S (2015) Survey paper on automatic speaker recognition systems. In: Inter- national conference on multimedia, computer graphics, and broadcasting international conference on signal processing, image processing, and pattern recognition, vol 4, pp 10895-10898
- ASVspoof consortium (2019) ASVspoof 2019: Automatic Speaker Verification Spoofing and Coun- termeasures Challenge Evaluation Plan. In: Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH 2019, pp 1-19
- Ramgire JB, Jagdale PSM (2016) A survey on speaker recognition with various feature extraction and classification techniques. Int Res J Eng Technol (IRJET) 3(4):709-712
- Sabhanayagam T, Prasanna Venkatesan V, Senthamaraikannan K (2018) A comprehensive survey on various biometric systems. Int J Appl Eng Res 13(5):2276-2297 101.
Cited by
Sorted by citations
The Vicomtech Audio Deepfake Detection System Based on Wav2vec2 for the 2022 ADD Challenge
Martin-Donas, J. M., & Alvarez, A. (2022) · ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp43922.2022.9747768 · Cited by 100 · see citing works
Khan, A., Malik, K. M., Ryan, J., & Saravanan, M. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10539-8 · Cited by 73 · see citing works
An improved feature extraction for Hindi language audio impersonation attack detection
Chakravarty, N., & Dua, M. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-023-18104-9 · Cited by 21 · see citing works
Ryu, R., Yeom, S., Herbert, D., & Dermoudy, J. (2023) · IEEE Access · doi:10.1109/access.2023.3253484 · Cited by 22 · see citing works
Explore the World of Audio Deepfakes: A Guide to Detection Techniques for Non-Experts
Schäfer, K., Choi, J., & Zmudzinski, S. (2024) · ACM International Workshop on Multimedia AI against Disinformation (MAD) · doi:10.1145/3643491.3660289 · Cited by 20 · see citing works
Martín-Doñas, J. M., Álvarez, A., Rosello, E., Gomez, A. M., & Peinado, A. M. (2024) · Interspeech 2024 · doi:10.21437/interspeech.2024-942 · Cited by 18 · see citing works
“These detection systems can help protect against malicious use of deepfakes for misinformation and identity theft, and even as countermeasures for automatic speaker verification systems [2, 3].”
BackgroundPartial Fake Speech Attacks in the Real World Using Deepfake Audio
AlAli, A., & Theodorakopoulos, G. (2025) · Journal of Cybersecurity and Privacy · doi:10.3390/jcp5010006 · Cited by 14 · see citing works
A replay attack detection scheme based on perceptual image hashing
Maamar, H., Zebbiche, K., Tebbi, H., Guerroumi, M., & Zafoune, Y. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-15300-5 · Cited by 12 · see citing works
Spoof Detection using Voice Contribution on LFCC features and ResNet-34
Mon, K. Z., Galajit, K., Mawalim, C. O., Karnjana, J., Isshiki, T., & Aimmanee, P. (2023) · International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP) · doi:10.1109/isai-nlp60301.2023.10354625 · Cited by 10 · see citing works
Uncovering the Deceptions: An Analysis on Audio Spoofing Detection and Future Prospects
Ranjan, R., Mayank Vatsa, A., & Singh, R. (2023) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2023/756 · Cited by 11 · see citing works
Noise robust automatic speaker verification systems: review and analysis
Joshi, S., & Dua, M. (2024) · Telecommunication Systems · doi:10.1007/s11235-024-01212-8 · Cited by 8 · see citing works
Differential convolutional network for noise mask estimation
Dişken, G. (2023) · Applied Acoustics · doi:10.1016/j.apacoust.2023.109568 · Cited by 7 · see citing works
Privacy-Preserving Occupancy Estimation
Williams, J. A., Yazdanpanah, V., & Stein, S. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10095340 · Cited by 6 · see citing works
A comparison of different support vector machine kernels for artificial speech detection
Tan, C. B., Hijazi, M. H. A., & Nohuddin, P. N. E. (2022) · TELKOMNIKA (Telecommunication Computing Electronics and Control) · doi:10.12928/telkomnika.v21i1.24259 · Cited by 7 · see citing works
Self-citationArtificial 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 · see citing works
Self-citationDetecting Replay Attack on Voice-Controlled Systems using Small Neural Networks
Ahmed, N., Khan, J., Sheta, N., Tarek, R., Zualkernan, I. A., & Aloul, F. (2022) · IEEE International Forum on Research and Technology for Society and Industry (RTSI) · doi:10.1109/rtsi55261.2022.9905158 · Cited by 5 · see citing works
Preserving Mental Health Information in Speech Anonymization
Ravuri, V., Gutiérrez‐Osuna, R., & Chaspari, T. (2022) · International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) · doi:10.1109/aciiw57231.2022.10086012 · Cited by 5 · see citing works
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 · see citing works
Self-citationLow-Key Shallow Learning Voice Spoofing Detection System
Ali, D., Al–Shareeda, S., & Abdulrahman, N. (2022) · IEEE Middle East and North Africa Communications Conference (MENACOMM) · doi:10.1109/menacomm57252.2022.9998199 · Cited by 4 · see citing works
Detection of cloned voices in realistic forensic voice comparison scenarios
Univaso, P., & Segundo, E. S. (2025) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2025.1678043 · Cited by 3 · see citing works
AFAD-MSA: Dataset and Models for Arabic Fake Audio Detection
Issa, E. (2026) · Computation · doi:10.3390/computation14010020 · Cited by 2 · see citing works
An Application-Oriented Taxonomy on Spoofing, D isguise and Countermeasures in Speaker Recognition
Li, L., Cheng, X.-L., & Zheng, T. F. (2022) · APSIPA Transactions on Signal and Information Processing · doi:10.1561/116.00000017 · Cited by 2 · see citing works
Chaudhari, A., Shedge, D., Bairagi, V. K., & Nanthaamornphong, A. (2024) · Symmetry · doi:10.3390/sym16070788 · Cited by 2 · see citing works
A Supervised Learning Classifier for Replayed Voice Attack Detection
Abdulrahman, N., Al–Shareeda, S., & Ali, D. (2022) · International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT) · doi:10.1109/3ict56508.2022.9990744 · Cited by 2 · see citing works
Chaudhari, A. A., Shedge, D. K., & Bairagi, V. K. (2023) · International Journal of Electrical and Electronics Engineering · doi:10.14445/23488379/ijeee-v10i10p112 · Cited by 2 · see citing works
On the Use of LSTM-RNN for Detecting Audio Spoofing Attacks
Huang, L. F., & Zhao, J. (2022) · International Conference on 3D Immersion, Interaction and Multi-sensory Experiences (ICDIIME) · doi:10.1109/icdiime56946.2022.00040 · Cited by 1 · see citing works
The Impact of Call Spoofing on Trust and Communication: A User Perception Study
Verma, A. (2024) · International Journal of Safety and Security Engineering · doi:10.18280/ijsse.140216 · Cited by 1 · see citing works
Detection of Audio Spoofing Attack Using Integrated Hand-Crafted Features with LSTM
Chakravarty, N., & Dua, M. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-4170-3_16 · Cited by 1 · see citing works
A qualitative systematic review of intra-speaker variation in the human voice
Segundo, E. S., & Hernández, J. D. (2025) · Journal of Speech Sciences · doi:10.20396/joss.v14i00.20738 · Cited by 1 · see citing works
The Vicomtech Spoofing-Aware Biometric System for the SASV Challenge
Martín-Doñas, J. M., Torre, I. G., Álvarez, A., & Arellano, J. (2022) · IberSPEECH · doi:10.21437/iberspeech.2022-14
Privacy-Preserving Speaker Verification and Speech Recognition
Abbasi, W. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-25467-3_7
A Comparative Evaluation on Data Transformation Approach for Artificial Speech Detection
Tan, C. B., & Hijazi, M. H. A. (2024) · ITM Web of Conferences · doi:10.1051/itmconf/20246301012
Self-citationLightweight Residual Network for Anti Spoofing in Speaker Verification
Wang, K., Fan, X., & Lei, Y. (2025) · International Conference on Cyber Security and Information Engineering (ICCSIE) · doi:10.1145/3759179.3759195
Takada, N. (2025) · Denshi Jouhou Tsuushin Gakkai Tsuushin Sosaieti magajin/B-plus/Denshi Jouhou Tsuushin Gakkai Tsuushin Sosaeti magajin · doi:10.1587/bplus.19.224
Bekiryazıcı, Ş., Hanilçi, C., & Ozcan, N. (2025) · Gümüşhane Üniversitesi Fen Bilimleri Enstitüsü Dergisi · doi:10.17714/gumusfenbil.1695733
End-to-End Modeling for Speech Spoofing and Deepfake Detection
Tak, H. (2023) · theses.fr (ABES) · doi:10.70675/aa9fb6cbz3f6az45bcz933fzf95d89893e59
Contactless Facial Mesh and Iris Landmark Analysis Vision-based Attendance System
Lokesh, G., Pallavi, T., Pravalika, V., Fathema, M., Naidu, V. D., & Maddilety, C. (2026) · International Conference on Expert Clouds and Applications (ICOECA) · doi:10.1109/icoeca68095.2026.11485333
Domain generalization for face anti-spoofing with CBAM and adaptive gradient alignment
Xing, H., Tan, S. Y., Qamar, F., & Jiao, Y. (2026) · Applied Intelligence · doi:10.1007/s10489-026-07324-9
Essaid, B., Kheddar, H., & Selouani, S. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7327979
Kaphungkui, N. K. (2026) · JOURNAL OF MECHANICS OF CONTINUA AND MATHEMATICAL SCIENCES · doi:10.26782/jmcms.2026.08.00012
Hybrid Voice Biometric Authentication Using Text Verification and Speaker Embeddings
Kupcová, E., Pleva, M., Sokolová, Z., & Sopkovič, K. (2026) · International Conference Radioelektronika · doi:10.1109/radioelektronika71189.2026.11726437
- Authors
- Tan, C. B., Hijazi, M. H. A., Khamis, N., Nohuddin, P. N. E. B., Zainol, Z., Coenen, F., & Gani, A.
- Affiliations
- Universiti of Malaysia Sabah (MY)
- National University of Malaysia (MY)
- National Defence University of Malaysia (MY)
- University of Liverpool (GB)
- Corresponding author
- Hijazi, Mohd Hanafi Ahmad
- ORCID
- Venue
- Multimedia Tools and Applications
- Published
- 2021-08-04
- Volume
- 80
- Issue
- 21-23
- Pages
- 32725-32762
- ISSN
- 1380-7501 (ISSN-L)
- 1573-7721
- OpenAlex
- W3187722398
- Cited by
- 40
- References
- 137
- Citations by year
- 2026: 6 · 2025: 7 · 2024: 6 · 2023: 11 · 2022: 10
- FWCI
- 2.39
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Multimedia Tools and Applications (publishedVersion, cc-by)
- Greater South Information System
- 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
- anti-spoofing, speaker verification, spoofing attacks, voice biometrics
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