Speaker Recognition Techniques: A Review

Todkar, Satyam P.; Babar, Snehal S.; Ambike, Rudrendra U.; Suryakar, Prasad B.; Prasad, J. Laxmi

doi:10.1109/i2ct.2018.8529519

Cited by 20 publications

(10 citation statements)

References 7 publications

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“…If they could get to the same level, they could work together with physical biometric modalities to increase security without yielding usability. There are several behavioral biometric modalities, such as voice recognition [4,5], handwritten signatures [6,7], key strokes [8,9], touch screen recognition [10,11], and gait recognition [12,13]. This article focuses on gait recognition, or the recognition of users as they walk.…”

Section: Introductionmentioning

confidence: 99%

Recurrent Neural Network for Inertial Gait User Recognition in Smartphones

Fernandez-Lopez

Liu-Jimenez

Kiyokawa

et al. 2019

Sensors

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In this article, a gait recognition algorithm is presented based on the information obtained from inertial sensors embedded in a smartphone, in particular, the accelerometers and gyroscopes typically embedded on them. The algorithm processes the signal by extracting gait cycles, which are then fed into a Recurrent Neural Network (RNN) to generate feature vectors. To optimize the accuracy of this algorithm, we apply a random grid hyperparameter selection process followed by a hand-tuning method to reach the final hyperparameter configuration. The different configurations are tested on a public database with 744 users and compared with other algorithms that were previously tested on the same database. After reaching the best-performing configuration for our algorithm, we obtain an equal error rate (EER) of 11.48% when training with only 20% of the users. Even better, when using 70% of the users for training, that value drops to 7.55%. The system manages to improve on state-of-the-art methods, but we believe the algorithm could reach a significantly better performance if it was trained with more visits per user. With a large enough database with several visits per user, the algorithm could improve substantially.

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Section: Introductionmentioning

confidence: 99%

Recurrent Neural Network for Inertial Gait User Recognition in Smartphones

Fernandez-Lopez

Liu-Jimenez

Kiyokawa

et al. 2019

Sensors

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“…FAR = 0.0%, FRR = 1.19% and ERR = 0.59% [48] Iris [49][50][51] Classification accuracy higher than 99.9%. FAR = 0,00001%, FRR = 0.1% [52] Retina [53,54] The true acceptance rate 98.148% [55] Face [56,57] FAR = 0,1%, FRR = 7% [52] Keystroke dynamics [58][59][60] Classification accuracy 92.60% [61] Signature dynamics [62,63] Average FAR = 5.125%, FRR = 5.5%, AER = 5.31% [64] Speech [65][66][67] Classification accuracy up to 99%. EER = 1% [68] The analysis of the presented values of the accuracy of authentication does not allow us to speak of a single use of features, however, it makes relevant their use within multimodal authentication (for example, face + iris [69], face and vein arrangement on finger, fingerprint, and voice [70], complex parameters of fingers and palms [71,72]) and the construction of ensembles of various types [73,74].…”

Section: Biometric Characteristic Papers Resultsmentioning

confidence: 99%

Information Security Methods—Modern Research Directions

et al. 2019

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In Tomsk University of Control Systems and Radioelectronics (TUSUR) one of the main areas of research is information security. The work is carried out by a scientific group under the guidance of Professor Shelupanov. One of the directions is the development of a comprehensive approach to assessing the security of the information systems. This direction includes the construction of an information security threats model and a protection system model, which allow to compile a complete list of threats and methods of protection against them. The main directions of information security tools development are dynamic methods of biometrics, methods for generating prime numbers for data encryption, steganography, methods and means of data protection in Internet of Things (IoT) systems. The article presents the main results of research in the listed areas of information security. The resultant properties in symmetric cryptography are based on the properties of the power of the generating functions. The authors have obtained symmetric principles for the development of primality testing algorithms, as discussed in the Appendix.Symmetry 2018, 10, x FOR PEER REVIEW 3 of 33 Ported to AMR system devices, IPsec ensures mutual authentication of network devices using the IKEv2 protocol. Optionally, the network can be configured based on the EAP-PSK protocol. During configuration, the devices receive network addresses and authentication keys, at which point the execution of EAP-PSK is stopped and data is transferred via IPsec. Another option is to use pre-installed certificates on the devices. In this case, the initial configuration is done manually, but the network does not require EAP-PSK to be used.Data integrity control and encryption during transmission are provided by ESP, which is the protocol used in IPsec at the transport level. This protocol ensures the security of both the data transmitted and packet headers at the network level.This approach makes it possible to ensure reliable authentication of the AMR system devices and the security of the data to be transmitted and opens a wide range of options for the configuration of network operation; however, it cannot be used in networks with heterogeneous communication channels. The EAP-PSK-based approach offers less flexibility but is suitable for networks with heterogeneous communication channels. For an AMR system, a list of threats was proposed based on the developed methodology. Threats to the confidentiality of the system are threats related to the collection of information about the system. This can be a list of devices, software versions, authentication data, access control policies, network addresses, interaction protocols, etc. Threats to the integrity of the automated system for commercial accounting are: substitution of an object, substitution of a communication channel, deletion of an object, destruction of a communication channel, addition of an unauthorized object, creation of an unauthorized communication channel; change of communication channel or object settings...

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“…There are many feature engineering methods for SR. Short‐term spectral features are the most widely used owing to their effectiveness—MFCC features 48 . In this study, we also experimented with other features, such as the chromagram, mel‐scaled spectrogram, and tonal centroid features (Tonnetz).…”

Section: Architecture Of Security Layersmentioning

confidence: 99%

Privacy‐preserving hands‐free voice authentication leveraging edge technology

Alattar¹,

Abbes²,

Zerai³

2022

Security and Privacy

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Although speech recognition technology has improved significantly over the past few years, the absence of reliable voice authentication methods has negatively affected the Internet of Things (IoT). Voice-activated devices, by design, rely on personal voice biometrics for access-level security, and insecure voice authentication techniques limit the development of voice interfaces that can deal with multiple users with different privileges. Furthermore, public concerns surrounding biometric systems have been reported. Outsourcing biometric data are liable to various privacy breaches, increasing vulnerability to cyberattacks from both criminal organizations and government agencies. In this article, we address the security and privacy challenges of voice authentication for IoT by presenting an edge-based, hands-free, single-factor authentication scheme that consists of three security-based features: random phrase verification, anti-spoof verification, and text-independent speaker recognition. The use of edge technology allows the integration of this authentication scheme with resource contained IoT devices such as voice assistants. Additionally, privacy is ensured by a novel security protocol that uses chaffing and encryption techniques to protect the users' biometric data stored locally on the edge device. Finally, the scheme does not compromise the user experience as it requires only two short voice inputs.

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Speaker Recognition Techniques: A Review

Cited by 20 publications

References 7 publications

Recurrent Neural Network for Inertial Gait User Recognition in Smartphones

Recurrent Neural Network for Inertial Gait User Recognition in Smartphones

Information Security Methods—Modern Research Directions

Privacy‐preserving hands‐free voice authentication leveraging edge technology

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