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

Author: Grafiati

Published: 7 June 2025

Last updated: 16 July 2025

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1

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. <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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<span lang="EN-US">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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Choon, Beng Tan, Hanafi Ahmad Hijazi Mohd, and Nor Ellyza Nohuddin Puteri. "A comparison of different support vector machine kernels for artificial speech detection." TELKOMNIKA (Telecommunication, Computing, Electronics and Control) 21, no. 1 (2023): 97–103. https://doi.org/10.12928/telkomnika.v21i1.24259.

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As the emergence of the voice biometric provides enhanced security and convenience, voice biometric-based applications such as speaker verification were gradually replacing the authentication techniques that were less secure. However, the automatic speaker verification (ASV) systems were exposed to spoofing attacks, especially artificial speech attacks that can be generated with a large amount in a short period of time using state-of-the-art speech synthesis and voice conversion algorithms. Despite the extensively used support vector machine (SVM) in recent works, there were none of the studie

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Go, Changhwan, Nam In Park, Oc-Yeub Jeon, and Chanjun Chun. "A Pre-Training Framework Based on Multi-Order Acoustic Simulation for Replay Voice Spoofing Detection." Sensors 23, no. 16 (2023): 7280. http://dx.doi.org/10.3390/s23167280.

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Voice spoofing attempts to break into a specific automatic speaker verification (ASV) system by forging the user’s voice and can be used through methods such as text-to-speech (TTS), voice conversion (VC), and replay attacks. Recently, deep learning-based voice spoofing countermeasures have been developed. However, the problem with replay is that it is difficult to construct a large number of datasets because it requires a physical recording process. To overcome these problems, this study proposes a pre-training framework based on multi-order acoustic simulation for replay voice spoofing detec

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Selin M. "Enhancing the Security of Speaker Verification: A Hybrid Feature and Xception-Based Method for Spoof Detection." Journal of Information Systems Engineering and Management 10, no. 31s (2025): 1006–13. https://doi.org/10.52783/jisem.v10i31s.5155.

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Even though Automatic Speaker Verification (ASV) systems are an essential part of biometric authentication, they are nevertheless vulnerable to spoofing attacks, particularly logical access attacks such as voice conversion and text-to-speech (TTS) synthesis. In order to increase ASV security, an effective spoof detection system is suggested that integrates the complementary data from Mel-Frequency Cepstral Coefficients (MFCC) and Constant Q Cepstral Coefficients (CQCC). The Xception model, the most advanced deep learning (DL) architecture created for high-dimensional extraction of feature, han

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Prihasto, Bima, Mifta Nur Farid, and Rafid Al Khairy. "Advancing Voice Anti-Spoofing Systems: Self-Supervised Learning and Indonesian Dataset Integration for Enhanced Generalization." Brilliance: Research of Artificial Intelligence 4, no. 2 (2025): 890–900. https://doi.org/10.47709/brilliance.v4i2.5182.

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This study examines how self-supervised learning and a novel Indonesian language dataset enhance anti-spoofing systems. Results show improved model performance, with a lower Equal Error Rate (EER) during training, indicating effective learning from diverse audio samples. Using weighted cross-entropy analysis highlights the model's robustness in minimizing training errors. Comparisons with baseline models using English data reveal the proposed approach's superiority, achieving a significantly lower EER due to the incorporation of language-specific data. The unique phonetic features of Indonesia

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Wei, Linqiang, Yanhua Long, Haoran Wei, and Yijie Li. "New Acoustic Features for Synthetic and Replay Spoofing Attack Detection." Symmetry 14, no. 2 (2022): 274. http://dx.doi.org/10.3390/sym14020274.

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With the rapid development of intelligent speech technologies, automatic speaker verification (ASV) has become one of the most natural and convenient biometric speaker recognition approaches. However, most state-of-the-art ASV systems are vulnerable to spoofing attack techniques, such as speech synthesis, voice conversion, and replay speech. Due to the symmetry distribution characteristic between the genuine (true) speech and spoof (fake) speech pair, the spoofing attack detection is challenging. Many recent research works have been focusing on the ASV anti-spoofing solutions. This work invest

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Tan, Choon Beng, and Mohd Hanafi Ahmad Hijazi. "A Comparative Evaluation on Data Transformation Approach for Artificial Speech Detection." ITM Web of Conferences 63 (2024): 01012. http://dx.doi.org/10.1051/itmconf/20246301012.

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The rise of voice biometrics has transformed user authentication and offered enhanced security and convenience while phasing out less secure methods. Despite these advancements, Automatic Speaker Verification (ASV) systems remain vulnerable to spoofing, particularly with artificial speech generated swiftly using advanced speech synthesis and voice conversion algorithms. A recent data transformation technique achieved an impressive Equal Error Rate (EER) of 1.42% on the ASVspoof 2019 Logical Access Dataset. While this approach predominantly relies on Support Vector Machine (SVM) as the backend

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Hernández-Nava, Carlos Alberto, Eric Alfredo Rincón-García, Pedro Lara-Velázquez, Sergio Gerardo de-los-Cobos-Silva, Miguel Angel Gutiérrez-Andrade, and Roman Anselmo Mora-Gutiérrez. "Voice spoofing detection using a neural networks assembly considering spectrograms and mel frequency cepstral coefficients." PeerJ Computer Science 9 (December 18, 2023): e1740. http://dx.doi.org/10.7717/peerj-cs.1740.

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Nowadays, biometric authentication has gained relevance due to the technological advances that have allowed its inclusion in many daily-use devices. However, this same advantage has also brought dangers, as spoofing attacks are now more common. This work addresses the vulnerabilities of automatic speaker verification authentication systems, which are prone to attacks arising from new techniques for the generation of spoofed audio. In this article, we present a countermeasure for these attacks using an approach that includes easy to implement feature extractors such as spectrograms and mel freq

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Ahmed, Ali Saadoon, and Arshad M. Khaleel. "Enhancing Voice Authentication with a Hybrid Deep Learning and Active Learning Approach for Deepfake Detection." Journal of Robotics and Control (JRC) 5, no. 6 (2024): 2002–14. https://doi.org/10.18196/jrc.v5i6.23502.

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This paper explores the application of active learning to enhance machine learning classifiers for spoofing detection in automatic speaker verification (ASV) systems. Leveraging the ASVspoof 2019 database, we integrate an active learning framework with traditional machine learning workflows, specifically focusing on Random Forest (RF) and Multilayer Perceptron (MLP) classifiers. The active learning approach was implemented by initially training models on a small subset of data and iteratively selecting the most uncertain samples for further training, which allowed the classifiers to refine the

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Pranita, Niraj Palsapure, Rajeswari Rajeswari, and Kumar Kempegowda Sandeep. "Deep feature synthesis approach using selective graph attention for replay attack voice spoofing detection." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 4 (2024): 4915–26. https://doi.org/10.11591/ijai.v13.i4.pp4915-4926.

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As voice-based authentication becomes increasingly integrated into security frameworks, establishing effective defenses against voice spoofing, particularly replay attacks, is more crucial than ever. This paper presents a novel comprehensive framework for replay attack detection that leverages the integration of advanced spectral-temporal feature extraction and graph-based feature processing mechanisms. The proposed system presents the design of a waveform encoder and a novel temporal residual unit for spectral and temporal feature extraction in synchronous. Further, an approach of selective a

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Khan M. K., Amjad Hassan, and P. S. Aithal. "Identification of Customer Through Voice Biometric System in Call Centres." International Journal of Intelligent Systems and Applications 16, no. 5 (2024): 68–78. http://dx.doi.org/10.5815/ijisa.2024.05.06.

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In recent times, there has been a growing emphasis on adjusting communication strategies to foster strong customer relationships. This shift is driven by intensified competition, market maturation, and swift advancements in business technology. Consequently, companies have established call centers to efficiently handle customer support and fulfil customer inquiries. A pivotal aspect of enhancing service quality within these call centers involves accurately identifying customers during their interactions. The primary objective of this study is to introduce a methodology for identifying customer

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Zhao, Cui, Zhenjiang Li, Han Ding, Wei Xi, Ge Wang, and Jizhong Zhao. "Anti-Spoofing Voice Commands." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 5, no. 3 (2021): 1–22. http://dx.doi.org/10.1145/3478116.

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This paper presents an anti-spoofing design to verify whether a voice command is spoken by one live legal user, which supplements existing speech recognition systems and could enable new application potentials when many crucial voice commands need a higher-standard verification in applications. In the literature, verifying the liveness and legality of the command's speaker has been studied separately. However, to accept a voice command from a live legal user, prior solutions cannot be combined directly due to two reasons. First, previous methods have introduced various sensing channels for the

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Wu, Zhizheng, Junichi Yamagishi, Tomi Kinnunen, et al. "ASVspoof: The Automatic Speaker Verification Spoofing and Countermeasures Challenge." IEEE Journal of Selected Topics in Signal Processing 11, no. 4 (2017): 588–604. http://dx.doi.org/10.1109/jstsp.2017.2671435.

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Zheng, Linlin, Jiakang Li, Meng Sun, Xiongwei Zhang, and Thomas Fang Zheng. "When Automatic Voice Disguise Meets Automatic Speaker Verification." IEEE Transactions on Information Forensics and Security 16 (2021): 824–37. http://dx.doi.org/10.1109/tifs.2020.3023818.

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Kinnunen, Tomi, Hector Delgado, Nicholas Evans, et al. "Tandem Assessment of Spoofing Countermeasures and Automatic Speaker Verification: Fundamentals." IEEE/ACM Transactions on Audio, Speech, and Language Processing 28 (2020): 2195–210. http://dx.doi.org/10.1109/taslp.2020.3009494.

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Hanilçi, Cemal. "Linear prediction residual features for automatic speaker verification anti-spoofing." Multimedia Tools and Applications 77, no. 13 (2017): 16099–111. http://dx.doi.org/10.1007/s11042-017-5181-0.

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Gada, Amay, Neel Kothari, Ruhina Karani, Chetashri Badane, Dhruv Gada, and Tanish Patwa. "DR-SASV: A deep and reliable spoof aware speech verification system." International Journal on Information Technologies and Security 15, no. 4 (2023): 93–106. http://dx.doi.org/10.59035/ffmb8272.

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A spoof-aware speaker verification system is an integrated system that is capable of jointly identifying impostor speakers as well as spoofing attacks from target speakers. This type of system largely helps in protecting sensitive data, mitigating fraud, and reducing theft. Research has recently enhanced the effectiveness of countermeasure systems and automatic speaker verification systems separately to produce low Equal Error Rates (EER) for each system. However, work exploring a combination of both is still scarce. This paper proposes an end-to-end solution to address spoof-aware automatic s

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Vestman, Ville, Tomi Kinnunen, Rosa González Hautamäki, and Md Sahidullah. "Voice Mimicry Attacks Assisted by Automatic Speaker Verification." Computer Speech & Language 59 (January 2020): 36–54. http://dx.doi.org/10.1016/j.csl.2019.05.005.

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Todisco, Massimiliano, Héctor Delgado, and Nicholas Evans. "Constant Q cepstral coefficients: A spoofing countermeasure for automatic speaker verification." Computer Speech & Language 45 (September 2017): 516–35. http://dx.doi.org/10.1016/j.csl.2017.01.001.

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Hanilçi, Cemal. "Data selection for i-vector based automatic speaker verification anti-spoofing." Digital Signal Processing 72 (January 2018): 171–80. http://dx.doi.org/10.1016/j.dsp.2017.10.010.

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Janicki, Artur, Federico Alegre, and Nicholas Evans. "An assessment of automatic speaker verification vulnerabilities to replay spoofing attacks." Security and Communication Networks 9, no. 15 (2016): 3030–44. http://dx.doi.org/10.1002/sec.1499.

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Kottursamy, Kottilingam. "Deep Learning based DFWF Model for Audio Spoofing Attack Detection." September 2022 4, no. 3 (2022): 179–87. http://dx.doi.org/10.36548/jaicn.2022.3.004.

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One of the biggest threats in the speaker verification system is that of fake audio attacks. Over the years several detection approaches have been introduced that were designed to provide efficient and spoof-proof data-specific scenarios. However, the speaker verification system is still exposed to fake audio threats. Hence to address this issue, several authors have proposed methodologies to retrain and finetune the input data. The drawback with retraining and fine-tuning is that retraining requires high computation resources and time while fine-tuning results in degradation of performance. M

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Shi, Yan, Juanjuan Zhou, Yanhua Long, Yijie Li, and Hongwei Mao. "Addressing Text-Dependent Speaker Verification Using Singing Speech." Applied Sciences 9, no. 13 (2019): 2636. http://dx.doi.org/10.3390/app9132636.

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The automatic speaker verification (ASV) has achieved significant progress in recent years. However, it is still very challenging to generalize the ASV technologies to new, unknown and spoofing conditions. Most previous studies focused on extracting the speaker information from natural speech. This paper attempts to address the speaker verification from another perspective. The speaker identity information was exploited from singing speech. We first designed and released a new corpus for speaker verification based on singing and normal reading speech. Then, the speaker discrimination was compa

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Yamagishi, Junichi, Tomi H. Kinnunen, Nicholas Evans, Phillip De Leon, and Isabel Trancoso. "Introduction to the Issue on Spoofing and Countermeasures for Automatic Speaker Verification." IEEE Journal of Selected Topics in Signal Processing 11, no. 4 (2017): 585–87. http://dx.doi.org/10.1109/jstsp.2017.2698143.

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Chadha, Ankita, Azween Abdullah, Lorita Angeline, and Sivakumar Sivanesan. "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. http://dx.doi.org/10.17485/ijst/v14i40.1279.

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Zhang, Xingyu, Xiongwei Zhang, Xia Zou, Haibo Liu, and Meng Sun. "Towards Generating Adversarial Examples on Combined Systems of Automatic Speaker Verification and Spoofing Countermeasure." Security and Communication Networks 2022 (July 31, 2022): 1–12. http://dx.doi.org/10.1155/2022/2666534.

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The security of unprotected automatic speaker verification (ASV) system is vulnerable to a variety of spoofing attacks where an attacker (adversary) disguises him/herself as a specific targeted user. It is a common practice to use spoofing countermeasure (CM) to improve the security of ASV systems so as to avoid illegal access. However, recent studies have shown that both ASV and CM systems are vulnerable to adversarial attacks. Previous researches mainly focus on adversarial attacks on a single ASV or CM system. But in practical scenarios, ASVs are typically deployed in conjunction with CM. I

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González Hautamäki, Rosa, Tomi Kinnunen, Ville Hautamäki, and Anne-Maria Laukkanen. "Automatic versus human speaker verification: The case of voice mimicry." Speech Communication 72 (September 2015): 13–31. http://dx.doi.org/10.1016/j.specom.2015.05.002.

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Gomez-Alanis, Alejandro, Jose A. Gonzalez-Lopez, S. Pavankumar Dubagunta, Antonio M. Peinado, and Mathew Magimai.-Doss. "On Joint Optimization of Automatic Speaker Verification and Anti-Spoofing in the Embedding Space." IEEE Transactions on Information Forensics and Security 16 (2021): 1579–93. http://dx.doi.org/10.1109/tifs.2020.3039045.

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Yu, Hong, Zheng-Hua Tan, Zhanyu Ma, Rainer Martin, and Jun Guo. "Spoofing Detection in Automatic Speaker Verification Systems Using DNN Classifiers and Dynamic Acoustic Features." IEEE Transactions on Neural Networks and Learning Systems 29, no. 10 (2018): 4633–44. http://dx.doi.org/10.1109/tnnls.2017.2771947.

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Mao, Hongwei, Yan Shi, Yue Liu, Linqiang Wei, Yijie Li, and Yanhua Long. "Short-time speaker verification with different speaking style utterances." PLOS ONE 15, no. 11 (2020): e0241809. http://dx.doi.org/10.1371/journal.pone.0241809.

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In recent years, great progress has been made in the technical aspects of automatic speaker verification (ASV). However, the promotion of ASV technology is still a very challenging issue, because most technologies are still very sensitive to new, unknown and spoofing conditions. Most previous studies focused on extracting target speaker information from natural speech. This paper aims to design a new ASV corpus with multi-speaking styles and investigate the ASV robustness to these different speaking styles. We first release this corpus in the Zenodo website for public research, in which each s

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Kadhim, Imad Burhan, Ali Najdet Nasret, and Zuhair Shakor Mahmood. "Enhancement and modification of automatic speaker verification by utilizing hidden Markov model." Indonesian Journal of Electrical Engineering and Computer Science 27, no. 3 (2022): 1397–403. https://doi.org/10.11591/ijeecs.v27.i3.pp1397-1403.

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The purpose of this study is to discuss the design and implementation of autonomous surface vehicle (ASV) systems. There’s a lot riding on the advancement and improvement of ASV applications, especially given the benefits they provide over other biometric approaches. Modern speaker recognition systems rely on statistical models like hidden Markov model (HMM), support vector machine (SVM), artificial neural networks (ANN), generalized method of moments (GMM), and combined models to identify speakers. Using a French dataset, this study investigates the effectiveness of prompted text speake

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Kamiński, Kamil Adam, Andrzej Piotr Dobrowolski, Zbigniew Piotrowski, and Przemysław Ścibiorek. "Enhancing Web Application Security: Advanced Biometric Voice Verification for Two-Factor Authentication." Electronics 12, no. 18 (2023): 3791. http://dx.doi.org/10.3390/electronics12183791.

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This paper presents a voice biometrics system implemented in a web application as part of a two-factor authentication (2FA) user login. The web-based application, via a client interface, runs registration, preprocessing, feature extraction and normalization, classification, and speaker verification procedures based on a modified Gaussian mixture model (GMM) algorithm adapted to the application requirements. The article describes in detail the internal modules of this ASR (Automatic Speaker Recognition) system. A comparison of the performance of competing ASR systems using the commercial NIST 2

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Kadhim, Imad Burhan, Ali Najdet Nasret, and Zuhair Shakor Mahmood. "Enhancement and modification of automatic speaker verification by utilizing hidden Markov model." Indonesian Journal of Electrical Engineering and Computer Science 27, no. 3 (2022): 1397. http://dx.doi.org/10.11591/ijeecs.v27.i3.pp1397-1403.

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<div class="WordSection1"><p>The purpose of this study is to discuss the design and implementation of autonomous surface vehicle (ASV) systems. There’s a lot riding on the advancement and improvement of ASV applications, especially given the benefits they provide over other biometric approaches. Modern speaker recognition systems rely on statistical models like hidden Markov model (HMM), support vector machine (SVM), artificial neural networks (ANN), generalized method of moments (GMM), and combined models to identify speakers. Using a French dataset, this study investigates the ef

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Kamiński, Kamil A., and Andrzej P. Dobrowolski. "Automatic Speaker Recognition System Based on Gaussian Mixture Models, Cepstral Analysis, and Genetic Selection of Distinctive Features." Sensors 22, no. 23 (2022): 9370. http://dx.doi.org/10.3390/s22239370.

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This article presents the Automatic Speaker Recognition System (ASR System), which successfully resolves problems such as identification within an open set of speakers and the verification of speakers in difficult recording conditions similar to telephone transmission conditions. The article provides complete information on the architecture of the various internal processing modules of the ASR System. The speaker recognition system proposed in the article, has been compared very closely to other competing systems, achieving improved speaker identification and verification results, on known cer

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Faham Ali Zaidi, Syed, and Longting Xu. "Implementation of Multiple Feature Selection Algorithms for Speech Spoofing Detection." Journal of Physics: Conference Series 2224, no. 1 (2022): 012119. http://dx.doi.org/10.1088/1742-6596/2224/1/012119.

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Abstract The ASVspoof challenge sequences were proposed to lead the research in anti-spoofing to a new level for automatic speaker verification (ASV). It’s verified that constant Q cepstral coefficients (CQCC) processes speech in variable frequencies with adjustable resolution and outperforms the other generally used features and Linear Frequency Cepstral Coefficient (LFCC) is used in high-frequency areas. The feature selection algorithm is offered to decrease computational complexity and overfitting for spoofed utterance detection. Precisely, there’s a demand for feature selection algorithms

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Njoku, Judith Nkechinyere, Cosmas Ifeanyi Nwakanma, Jae-Min Lee, and Dong-Seong Kim. "Enhancing Security and Accountability in Autonomous Vehicles through Robust Speaker Identification and Blockchain-Based Event Recording." Electronics 12, no. 24 (2023): 4998. http://dx.doi.org/10.3390/electronics12244998.

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As the deployment of Autonomous Vehicles (AVs) gains momentum, ensuring both security and accountability becomes paramount. This paper proposes a comprehensive approach to address these concerns. With the increasing importance of speaker identification, our first contribution lies in implementing a robust mechanism for identifying authorized users within AVs, enhancing security. To counter the threat of voice spoofing, an ensemble-based approach leveraging speaker verification techniques is presented, ensuring the authenticity of user commands. Furthermore, in scenarios of accidents involving

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Zhang, Xingyu, Xiongwei Zhang, Wei Liu, Xia Zou, Meng Sun, and Jian Zhao. "Waveform level adversarial example generation for joint attacks against both automatic speaker verification and spoofing countermeasures." Engineering Applications of Artificial Intelligence 116 (November 2022): 105469. http://dx.doi.org/10.1016/j.engappai.2022.105469.

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Li, Jiakang, Meng Sun, Xiongwei Zhang, and Yimin Wang. "Joint Decision of Anti-Spoofing and Automatic Speaker Verification by Multi-Task Learning With Contrastive Loss." IEEE Access 8 (2020): 7907–15. http://dx.doi.org/10.1109/access.2020.2964048.

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Maciejko, Waldemar. "The Effect of Voice over IP Transmission Degradations on MAP-EM-GMM Speaker Verification Performance." Archives of Acoustics 40, no. 3 (2015): 407–17. http://dx.doi.org/10.1515/aoa-2015-0042.

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Abstract Despite the growing importance of packet switching systems, there is still a shortage of thorough analyses of VoIP transmission effect on speech and speaker recognition performance. Voice over IP transmission systems use packet switching. There is no guarantee of delivery. The main disadvantage of VoIP is a packet loss which has a major impact on the performance experienced by the users of the network. There are several techniques to mask the effects of a packet loss, referred to as packet loss concealment. In this study, the effect of voice transmission over IP on automatic speaker v

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Dr., Zaw Win Aung. "Automatic Attendance System Using Speaker Recognition." International Journal of Trend in Scientific Research and Development 2, no. 6 (2018): 802–6. https://doi.org/10.31142/ijtsrd18763.

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The main aim of this paper is to develop automatic attendance system using speaker recognition technique. The proposed system is software architecture which allows the user to access the system by making an utterance from microphone and the attendance of corresponding user is marked in the Microsoft Office Excel. The proposed system automates the whole process of taking attendance. The system uses text dependent open set speaker identification with MFCC features and vector quantization based speaker modeling for authenticating the user. A simple Euclidean distance scoring is used as the classi

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Zhang, Jiachen, Guoqing Tu, Shubo Liu, and Zhaohui Cai. "Audio Anti-Spoofing Based on Audio Feature Fusion." Algorithms 16, no. 7 (2023): 317. http://dx.doi.org/10.3390/a16070317.

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The rapid development of speech synthesis technology has significantly improved the naturalness and human-likeness of synthetic speech. As the technical barriers for speech synthesis are rapidly lowering, the number of illegal activities such as fraud and extortion is increasing, posing a significant threat to authentication systems, such as automatic speaker verification. This paper proposes an end-to-end speech synthesis detection model based on audio feature fusion in response to the constantly evolving synthesis techniques and to improve the accuracy of detecting synthetic speech. The mode

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