Voice Recognition and Authentication as a Proficient Biometric Tool and Its Application in Online Exam for P.H People

Rudrapal, Dwijen; Das, Smita; Debbarma, Swapan; Kar, Nirmalya; Debbarma, Nuna

doi:10.5120/4870-7297

Cited by 32 publications

(21 citation statements)

References 7 publications

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“…Other physiological parameters available in the field of biometrics include vein recognition [24], ear recognition [25], palm print recognition [26], hand geometry [27], finger geometry [28], lip furrows [29,30], DNA [31], and odour/scent recognition [32]. Behavioural parameters that can be useful in biometric-detection of an individual include keyboard dynamics [33], gait [34], voice recognition [35], and signature [36,37].…”

Section: Other Methodsmentioning

confidence: 99%

Integration of Biometrics and Steganography: A Comprehensive Review

et al. 2019

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The use of an individual’s biometric characteristics to advance authentication and verification technology beyond the current dependence on passwords has been the subject of extensive research for some time. Since such physical characteristics cannot be hidden from the public eye, the security of digitised biometric data becomes paramount to avoid the risk of substitution or replay attacks. Biometric systems have readily embraced cryptography to encrypt the data extracted from the scanning of anatomical features. Significant amounts of research have also gone into the integration of biometrics with steganography to add a layer to the defence-in-depth security model, and this has the potential to augment both access control parameters and the secure transmission of sensitive biometric data. However, despite these efforts, the amalgamation of biometric and steganographic methods has failed to transition from the research lab into real-world applications. In light of this review of both academic and industry literature, we suggest that future research should focus on identifying an acceptable level steganographic embedding for biometric applications, securing exchange of steganography keys, identifying and address legal implications, and developing industry standards.

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Section: Other Methodsmentioning

confidence: 99%

Integration of Biometrics and Steganography: A Comprehensive Review

et al. 2019

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“…Few researchers have also focused on authenticating along with recognizing an individual's voice by comparing the collected voice template for training and testing [27,28]. However, architecture performance was not reported for noisy or real-time environments.…”

Section: Related Workmentioning

confidence: 99%

A Near Real-Time Automatic Speaker Recognition Architecture for Voice-Based User Interface

Dhakal

Damacharla

Javaid

et al. 2019

MAKE

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In this paper, we present a novel pipelined near real-time speaker recognition architecture that enhances the performance of speaker recognition by exploiting the advantages of hybrid feature extraction techniques that contain the features of Gabor Filter (GF), Convolution Neural Networks (CNN), and statistical parameters as a single matrix set. This architecture has been developed to enable secure access to a voice-based user interface (UI) by enabling speaker-based authentication and integration with an existing Natural Language Processing (NLP) system. Gaining secure access to existing NLP systems also served as motivation. Initially, we identify challenges related to real-time speaker recognition and highlight the recent research in the field. Further, we analyze the functional requirements of a speaker recognition system and introduce the mechanisms that can address these requirements through our novel architecture. Subsequently, the paper discusses the effect of different techniques such as CNN, GF, and statistical parameters in feature extraction. For the classification, standard classifiers such as Support Vector Machine (SVM), Random Forest (RF) and Deep Neural Network (DNN) are investigated. To verify the validity and effectiveness of the proposed architecture, we compared different parameters including accuracy, sensitivity, and specificity with the standard AlexNet architecture.

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“…For physically challenged people who can use their voice only to register and attend the examination, one way to conduct online examination was offered in [11]. Here an efficient spectrum analysis to continuous-time signal based on DFT, the Fast Fourier Transform (FFT) method was adopted for authentication of voice and concluded that The Fast Fourier Transform (FFT) was a much faster mathematical algorithm which eliminates redundant calculations in the Fourier Transform and was therefore much speedier.…”

Section: Feature Extraction Techniques 21 Fast Fourier Transform (Fft)mentioning

confidence: 99%

Feature Extraction Techniques in Speech Processing: A Survey

Hibare¹,

Vibhute²

2014

IJCA

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Speech processing includes the various techniques such as speech coding, speech synthesis, speech recognition and speaker recognition. In the area of digital signal processing, speech processing has versatile applications so it is still an intensive field of research. Speech processing mostly performs two fundamental operations such as Feature Extraction and Classification. The main criterion for the good speech processing system is the selection of feature extraction technique which plays an important role in the system accuracy. This paper intends to focus on the survey of various feature extraction techniques in speech processing such as Fast Fourier Transforms, Linear Predictive Coding, Mel Frequency Cepstral Coefficients, Discrete Wavelet Transforms, Wavelet Packet Transforms, Hybrid Algorithm DWPD and their applications in speech processing.

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Voice Recognition and Authentication as a Proficient Biometric Tool and Its Application in Online Exam for P.H People

Cited by 32 publications

References 7 publications

Integration of Biometrics and Steganography: A Comprehensive Review

Integration of Biometrics and Steganography: A Comprehensive Review

A Near Real-Time Automatic Speaker Recognition Architecture for Voice-Based User Interface

Feature Extraction Techniques in Speech Processing: A Survey

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