Voice Biometric Identity Authentication System Based on Android Smart Phone

Zhang, Xinman; Xiong, Qingrong; Dai, Yixuan; Xu, Xuebin

doi:10.1109/compcomm.2018.8780990

Cited by 17 publications

(4 citation statements)

References 6 publications

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“…When the number of training samples is 5, the system authentication success rate is between 89 and 96 percent, and the time required to authenticate a speech sample is between 210 and 320 milliseconds, demonstrating excellent accuracy and good real-time performance. [34] 7…”

Section: Discussion Of Related Workmentioning

confidence: 99%

Voice Biometric Systems for User Identification and Authentication – A Literature Review

K.¹,

Aithal

2022

IJAEML

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Purpose: Biometric trends are used in many systems because of security aspects. The cryptosystem is such an example which uses a biometric. But due to stored biometric data for the authentication, this can be a dangerous issue. Therefore, in comparison to conventional used biometric system, voice biometric system provides an efficient safety, security and unique identity. Among various speech recognition or processing methods, there is one called automated speech conversion methods, which also used to convert the recorded voice into text format. The overall concept of voice reorganization and voice biometric system is based on the acoustic modelling. Therefore, for getting the perfect speech detection, robust acoustic modelling is required. Our analysis describes the advancement and usage of voice biometric system for user identification and authentication. This paper provides a descriptive review of different voice biometric systems, their advancement and applications in different fields. Methodology: The core principles of the research issue have been well discussed in the literature review on speech biometrics. During this process, selected journals from a variety of secondary data sources, such as research papers published in a variety of reputed journals periodicals that are related to the topic are studied in the methodology. Findings/Result: A vocal biometric system is a biological system that captures an individual's voice and assigns it a unique characteristic for authentication purposes. This speech biometric method is primarily used to provide secure, quick, and frictionless access to various electronic devices. In the last three years, rapid technological advancements in neural networks have improved the deployment of speech biometric systems in a variety of industries. The majority of speech biometric system designs are based on the CPU, necessary power, and memory concepts. The advancement of software and hardware interface has been dramatically enhanced and implemented for many applications in the last few years, including smart watches, mobile phones, and car locking systems, where the interface between humans and electronics devices is critical. Banking security, attendance system, file access system, security control, and forensic development system are some of the other commercial applications. Originality: Following the literature study, the findings were utilized to conclude that, despite advances in biometric technology, there is still a significant gap in practical application, particularly for voice biometric systems. When building and developing a voice biometric system, it is necessary to integrate it with an IoT system. Paper Type: Literature Review.

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Section: Discussion Of Related Workmentioning

confidence: 99%

Voice Biometric Systems for User Identification and Authentication – A Literature Review

K.¹,

Aithal

2022

IJAEML

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“…There are two ways of removing noise from collected sound data. That is with the use threshold based de-noising method [35,36,37] and recursive least squares adaptive filtering method [38,39]. However, the first is adopted in our work for its suitability of smart devices.…”

Section: Pre-processingmentioning

confidence: 99%

Untitled

2020

IJSPTM

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Behavioral biometric authentication is considered as a promising approach to securing the internet of things (IoT) ecosystem. In this paper, we investigated the need and suitability of employing voice recognition systems in the user authentication of the IoT. Tools and techniques used in accomplishing voice recognition systems are reviewed, and their appropriateness to the IoT environment are discussed. In the end, a voice recognition system is proposed for IoT ecosystem user authentication. The proposed system has two phases. The first being the enrollment phase consisting of a pre-processing step where the noise is removed from the voice for the enrollment process, the feature extraction step where feature traits are extracted from user's voice, and the model training step where the voice model is trained for the IoT user. And the second being the phase verifies whether the identity claimer is the owner of the IoT device. Based on the resources limitedness of the IoT technologies, the suitability of text-dependent voice recognition systems is promoted. Likewise, the use of MFCC features is considered in the proposed system.

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“…For these systems, our experiments can be useful in terms of confirming the classifiers' completeness. One example is in classification systems based on speech characteristics [10][11][12][13][14][15]. Nevertheless, research in the field of adversarial attacks on such systems [16,17] proves that even the use of biometric metrics such as voice is not a guarantee of protection from adversarial attacks.…”

Section: Introductionmentioning

confidence: 99%

Adversarial Attacks Impact on the Neural Network Performance and Visual Perception of Data under Attack

et al. 2022

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Machine learning algorithms based on neural networks are vulnerable to adversarial attacks. The use of attacks against authentication systems greatly reduces the accuracy of such a system, despite the complexity of generating a competitive example. As part of this study, a white-box adversarial attack on an authentication system was carried out. The basis of the authentication system is a neural network perceptron, trained on a dataset of frequency signatures of sign. For an attack on an atypical dataset, the following results were obtained: with an attack intensity of 25%, the authentication system availability decreases to 50% for a particular user, and with a further increase in the attack intensity, the accuracy decreases to 5%.

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Voice Biometric Identity Authentication System Based on Android Smart Phone

Cited by 17 publications

References 6 publications

Voice Biometric Systems for User Identification and Authentication – A Literature Review

Voice Biometric Systems for User Identification and Authentication – A Literature Review

Untitled

Adversarial Attacks Impact on the Neural Network Performance and Visual Perception of Data under Attack

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