Relevant bibliographies by topics / Automatic speaker verification voice spoofing

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  2. Dissertations / Theses
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Academic literature on the topic 'Automatic speaker verification voice spoofing'

Author: Grafiati
Published: 7 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Automatic speaker verification voice spoofing"

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Nafees, Muhammad, Abid Rauf, and Rabbia Mahum. "Automatic Spoofing Detection Using Deep Learning." Global Social Sciences Review IX, no. I (2024): 111–333. http://dx.doi.org/10.31703/gssr.2024(ix-i).11.

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Deep fakes stand out to be the most dangerous side effects of Artificial Intelligence. AI assists to produce voice cloning of any entity which is very arduous to categorize whether it’s fake or real. The aim of the research is to impart a spoofing detection system to an automatic speaker verification (ASV) system that can perceive false voices efficiently. The goal is to perceive the unapparent audio elements with maximum precision and to develop a model that is proficient in automatically extracting audio features by utilizing the ASVspoof 2019 dataset. Hence, the proposed ML-DL SafetyNet mod
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Ankita, Chadha, Abdullah Azween, Angeline Lorita, and Sivanesan Sivakumar. "A review on state-of-the-art Automatic Speaker verification system from spoofing and anti-spoofing perspective." Indian Journal of Science and Technology 14, no. 40 (2021): 3026–50. https://doi.org/10.17485/IJST/v14i40.1279.

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Abstract <strong>Background/Objectives</strong>: The anti-spoofing measures are blooming with an aim to protect the Automatic Speaker Verification systems from susceptible spoofing attacks. This review is an amalgam of the possible attack types, the datasets required, the renowned feature representation techniques, modeling algorithms involving machine learning, and score normalization techniques.&nbsp;<strong>Method/Findings</strong>: A detailed analysis of existing datasets is carried based on the total speaker samples, the number of speakers, and source of availability- open or licensed. Th
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Ravindran, Swathika, and K. Geetha. "An Overview of Spoof Detection in ASV Systems." ECS Transactions 107, no. 1 (2022): 1963–71. http://dx.doi.org/10.1149/10701.1963ecst.

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In current years, voice based application are used broadly in varied applications for speaker recognition. By and by, there is a wide work in the investigation of parodying and against mocking for Automatic Speaker Verification (ASV) framework. The current advancement within the ASV system ends up interest to secure these voice biometric systems for existent world applications. This paper provides the literature of spoofing detection, novel acoustic feature representations, deep learning, end-to-end systems, etc. Moreover, it conjointly summaries previous studies of spoofing attacks with stres
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Pranita, Niraj Palsapure, Rajeswari Rajeswari, Kumar Kempegowda Sandeep, and Trupti Ravikumar Kumbhar. "Discriminative deep learning based hybrid spectro-temporal features for synthetic voice spoofing detection." IAES International Journal of Artificial Intelligence (IJ-AI) 14, no. 1 (2025): 130–41. https://doi.org/10.11591/ijai.v14.i1.pp130-141.

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Voice-based systems like speaker identification systems (SIS) and automatic speaker verification systems (ASV) are proliferating across industries such as finance and healthcare due to their utility in identity verification through unique speech pattern analysis. Despite their advancements, ASVs are susceptible to various spoofing attacks, including logical and replay attacks, posing challenges due to the sophisticated acoustic distinctions between authentic and spoofed voices. To counteract, this study proposes a robust yet computationally efficient countermeasure system, utilizing a systemat
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Alam. "On the Use of Fisher Vector Encoding for Voice Spoofing Detection." Proceedings 31, no. 1 (2019): 37. http://dx.doi.org/10.3390/proceedings2019031037.

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Recently, the vulnerability of automatic speaker recognition systems to spoofing attacks has received significant interest among researchers. A robust speaker recognition system demands not only high recognition accuracy but also robustness to spoofing attacks. Several spoofing and countermeasure challenges have been organized to draw attention to this problem among the speaker recognition communities. Low-level descriptors designed to detect artifacts in spoofed speech are found to be the most effective countermeasures against spoofing attacks. In this work, we used Fisher vector encoding of
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Palsapure, Pranita Niraj, Rajeswari Rajeswari, Sandeep Kumar Kempegowda, and Kumbhar Trupti Ravikumar. "Discriminative deep learning based hybrid spectro-temporal features for synthetic voice spoofing detection." IAES International Journal of Artificial Intelligence (IJ-AI) 14, no. 1 (2025): 130. http://dx.doi.org/10.11591/ijai.v14.i1.pp130-141.

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&lt;span lang="EN-US"&gt;Voice-based systems like speaker identification systems (SIS) and automatic speaker verification systems (ASV) are proliferating across industries such as finance and healthcare due to their utility in identity verification through unique speech pattern analysis. Despite their advancements, ASVs are susceptible to various spoofing attacks, including logical and replay attacks, posing challenges due to the sophisticated acoustic distinctions between authentic and spoofed voices. To counteract, this study proposes a robust yet computationally efficient countermeasure sys
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Altuwayjiri, Sarah Mohammed, Ouiem Bchir, and Mohamed Maher Ben Ismail. "Generalized Replay Spoofing Countermeasure Based on Combining Local Subclassification Models." Applied Sciences 12, no. 22 (2022): 11742. http://dx.doi.org/10.3390/app122211742.

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Automatic speaker verification (ASV) systems play a prominent role in the security field due to the usability of voice biometrics compared to alternative biometric authentication modalities. Nevertheless, ASV systems are susceptible to malicious voice spoofing attacks. In response to such threats, countermeasures have been devised to prevent breaches and ensure the safety of user data by categorizing utterances as either genuine or spoofed. In this paper, we propose a new voice spoofing countermeasure that seeks to improve the generalization of supervised learning models. This is accomplished
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Gomez-Alanis, Alejandro, Jose A. Gonzalez-Lopez, and Antonio M. Peinado. "GANBA: Generative Adversarial Network for Biometric Anti-Spoofing." Applied Sciences 12, no. 3 (2022): 1454. http://dx.doi.org/10.3390/app12031454.

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Automatic speaker verification (ASV) is a voice biometric technology whose security might be compromised by spoofing attacks. To increase the robustness against spoofing attacks, presentation attack detection (PAD) or anti-spoofing systems for detecting replay, text-to-speech and voice conversion-based spoofing attacks are being developed. However, it was recently shown that adversarial spoofing attacks may seriously fool anti-spoofing systems. Moreover, the robustness of the whole biometric system (ASV + PAD) against this new type of attack is completely unexplored. In this work, a new genera
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Qadir, Gulam, Saima Zareen, Farman Hassan, and Auliya Ur Rahman. "Voice Spoofing Countermeasure Based on Spectral Features to Detect Synthetic Attacks Through LSTM." Vol 3 Issue 5 3, no. 5 (2022): 153–65. http://dx.doi.org/10.33411/ijist/2021030512.

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With the growing number of voice-controlled devices, it is necessary to address the potential vulnerabilities of Automatic Speaker Verification (ASV) against voice spoofing attacks such as Physical Access (PA) and Logical Access (LA) attacks. To improve the reliability of ASV systems, researchers have developed various voice spoofing countermeasures. However, it is hard for the voice anti-spoofing systems to effectively detect the synthetic speech attacks that are generated through powerful spoofing algorithms and have quite different statistical distributions. More importantly, the speedy imp
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Yuan, Junming, Mijit Ablimit, and Askar Hamdulla. "Replay Attack Detection Based on High Frequency Missing Spectrum." Information 14, no. 1 (2022): 7. http://dx.doi.org/10.3390/info14010007.

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Automatic Speaker Verification (ASV) has its benefits compared to other biometric verification methods, such as face recognition. It is convenient, low cost, and more privacy protected, so it can start being used for various practical applications. However, voice verification systems are vulnerable to unknown spoofing attacks, and need to be upgraded with the pace of forgery techniques. This paper investigates a low-cost attacking scenario in which a playback device is used to impersonate the real speaker. The replay attack only needs a recording and playback device to complete the process, so
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Dissertations / Theses on the topic "Automatic speaker verification voice spoofing"

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Ge, Wanying. "Spoofing-robust Automatic Speaker Verification : Architecture, Explainability and Joint Optimisation." Electronic Thesis or Diss., Sorbonne université, 2024. http://www.theses.fr/2024SORUS071.

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Cette thèse explore les systèmes de vérification automatique du locuteur (ASV) et leurs vulnérabilités aux attaques de spoofing, soulignant la nécessité de contre-mesures robustes contre le spoofing (CMs). Elle présente l'application de la recherche d'architecture différentiable partiellement connectée (PC-DARTS) pour optimiser les architectures de réseau pour la lutte contre l'usurpation de la voix, démontrant une performance compétitive et une meilleure généralisation contre les attaques non vues. En outre, il utilise SHapley Additive exPlanations (SHAP) pour analyser et visualiser l'impact
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Patino, Villar José María. "Efficient speaker diarization and low-latency speaker spotting." Electronic Thesis or Diss., Sorbonne université, 2019. http://www.theses.fr/2019SORUS003.

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La segmentation et le regroupement en locuteurs (SRL) impliquent la détection des locuteurs dans un flux audio et les intervalles pendant lesquels chaque locuteur est actif, c'est-à-dire la détermination de ‘qui parle quand’. La première partie des travaux présentés dans cette thèse exploite une approche de modélisation du locuteur utilisant des clés binaires (BKs) comme solution à la SRL. La modélisation BK est efficace et fonctionne sans données d'entraînement externes, car elle utilise uniquement des données de test. Les contributions présentées incluent l'extraction des BKs basée sur l'ana
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Patino, Villar José María. "Efficient speaker diarization and low-latency speaker spotting." Thesis, Sorbonne université, 2019. http://www.theses.fr/2019SORUS003/document.

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La segmentation et le regroupement en locuteurs (SRL) impliquent la détection des locuteurs dans un flux audio et les intervalles pendant lesquels chaque locuteur est actif, c'est-à-dire la détermination de ‘qui parle quand’. La première partie des travaux présentés dans cette thèse exploite une approche de modélisation du locuteur utilisant des clés binaires (BKs) comme solution à la SRL. La modélisation BK est efficace et fonctionne sans données d'entraînement externes, car elle utilise uniquement des données de test. Les contributions présentées incluent l'extraction des BKs basée sur l'ana
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Books on the topic "Automatic speaker verification voice spoofing"

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Meisel, William S. The telephony voice user interface: Applications of speech recognition, text-to-speech, and speaker verification over the telephone. TMA Associates, 1998.

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Book chapters on the topic "Automatic speaker verification voice spoofing"

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Lavrentyeva, Galina, Sergey Novoselov, and Konstantin Simonchik. "Anti-spoofing Methods for Automatic Speaker Verification System." In Communications in Computer and Information Science. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-52920-2_17.

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Uparakool, Peemapot, Waree Kongprawechnon, Noppharut Pipopsophonchai, et al. "Anti-spoofing Using ResNet50 with Linear Discriminant Analysis for Automatic Speaker Verification." In Lecture Notes in Computer Science. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-4606-7_24.

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Hao, Bin, and Xiali Hei. "Voice Liveness Detection for Medical Devices." In Advances in Medical Technologies and Clinical Practice. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-7525-2.ch005.

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Many healthcare providers integrate biometric recognition/verification schemes into patient identification or other information security systems. While overcoming the disadvantages of using passwords, PINs, and tokens which may be forgotten, or stolen, biometric systems are susceptible to spoofing attacks, or presentation attacks. Liveness detection is an effective mechanism used to defeat a presentation attack. This chapter focuses on voice liveness detection in automatic speaker verification (ASV) systems. The authors explain the spoofing attacks to ASV systems comprising impersonation, voic
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Sinha, Keshav, Rasha Subhi Hameed, Partha Paul, and Karan Pratap Singh. "Voice-Based Speaker Identification and Verification." In Advances in Library and Information Science. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-7258-0.ch016.

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In recent years, the advancement in voice-based authentication leads in the field of numerous forensic voice authentication technology. For verification, the speech reference model is collected from various open-source clusters. In this chapter, the primary focus is on automatic speech recognition (ASR) technique which stores and retrieves the data and processes them in a scalable manner. There are the various conventional techniques for speech recognition such as BWT, SVD, and MFCC, but for automatic speech recognition, the efficiency of these conventional recognition techniques degrade. So,
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Chakravarty, Nidhi, and Mohit Dua. "Securing Automatic Speaker Verification Systems Using Residual Networks." In Advances in Information Security, Privacy, and Ethics. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-2223-9.ch005.

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Spoofing attacks are a major risk for automatic speaker verification systems, which are becoming more widespread. Adequate countermeasures are necessary since attacks like replay, synthetic, and deepfake attacks, are difficult to identify. Technologies that can identify audio-level attacks must be developed in order to address this issue. In this chapter, the authors have proposed combination of different spectrogram-based techniques with Residual Networks34 (ResNet34) for securing the automatic speaker verification (ASV) systems. The methodology uses Mel frequency scale-based Mel-spectrogram
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Salimbajevs, Askars. "Using Privacy-Transformed Speech in the Automatic Speech Recognition Acoustic Model Training." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2020. http://dx.doi.org/10.3233/faia200601.

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Automatic Speech Recognition (ASR) requires huge amounts of real user speech data to reach state-of-the-art performance. However, speech data conveys sensitive speaker attributes like identity that can be inferred and exploited for malicious purposes. Therefore, there is an interest in the collection of anonymized speech data that is processed by some voice conversion method. In this paper, we evaluate one of the voice conversion methods on Latvian speech data and also investigate if privacy-transformed data can be used to improve ASR acoustic models. Results show the effectiveness of voice co
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Conference papers on the topic "Automatic speaker verification voice spoofing"

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VS, Krithikaa Venket, and Safia Naveed. "A Review of Automatic Speaker Verification Systems with Feature Extractions and Spoofing Attacks." In 2024 5th International Conference on Electronics and Sustainable Communication Systems (ICESC). IEEE, 2024. http://dx.doi.org/10.1109/icesc60852.2024.10690005.

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Dao, Anh-Tuan, Mickael Rouvier, and Driss Matrouf. "ASVspoof 5 Challenge: advanced ResNet architectures for robust voice spoofing detection." In The Automatic Speaker Verification Spoofing Countermeasures Workshop (ASVspoof 2024). ISCA, 2024. http://dx.doi.org/10.21437/asvspoof.2024-24.

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Xia, Weijiang, Haipeng Peng, Lixiang Li, and Yeqing Ren. "A single end-to-end voice anti-spoofing model with graph attention and feature aggregation for ASVspoof 5 Challenge." In The Automatic Speaker Verification Spoofing Countermeasures Workshop (ASVspoof 2024). ISCA, 2024. http://dx.doi.org/10.21437/asvspoof.2024-18.

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Alegre, Federico, Asmaa Amehraye, and Nicholas Evans. "Spoofing countermeasures to protect automatic speaker verification from voice conversion." In ICASSP 2013 - 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2013. http://dx.doi.org/10.1109/icassp.2013.6638222.

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Phyu, Win Lai Lai, Win Pa Pa, and Hay Mar Soe Naing. "Automatic Speaker Verification on Myanmar Spoofing Voice Data using GMM-UBM and TDNN." In MMAsia'24: ACM Multimedia Asia Workshops. ACM, 2024. https://doi.org/10.1145/3700410.3702121.

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Monteiro, Joao, and Jahangir Alam. "Development of Voice Spoofing Detection Systems for 2019 Edition of Automatic Speaker Verification and Countermeasures Challenge." In 2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU). IEEE, 2019. http://dx.doi.org/10.1109/asru46091.2019.9003792.

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Panda, Soumya Priyadarsini, and Krishna Singh. "Automatic Speaker Verification Under Spoofing Attack." In 2021 19th OITS International Conference on Information Technology (OCIT). IEEE, 2021. http://dx.doi.org/10.1109/ocit53463.2021.00047.

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Evans, Nicholas, Tomi Kinnunen, and Junichi Yamagishi. "Spoofing and countermeasures for automatic speaker verification." In Interspeech 2013. ISCA, 2013. http://dx.doi.org/10.21437/interspeech.2013-288.

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Wu, Zhizheng, and Haizhou Li. "Voice conversion and spoofing attack on speaker verification systems." In 2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA). IEEE, 2013. http://dx.doi.org/10.1109/apsipa.2013.6694344.

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Ergunay, Serife Kucur, Elie Khoury, Alexandros Lazaridis, and Sebastien Marcel. "On the vulnerability of speaker verification to realistic voice spoofing." In 2015 IEEE 7th International Conference on Biometrics Theory, Applications and Systems (BTAS). IEEE, 2015. http://dx.doi.org/10.1109/btas.2015.7358783.

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