An information set-based robust text-independent speaker authentication

Medikonda, Jeevan; Bhardwaj, Saurabh; Hanmandlu, M.

doi:10.1007/s00500-019-04277-9

Cited by 4 publications

(2 citation statements)

References 35 publications

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“…The chief limitation behind in this model is similar voice features were trained to verify the own users. Thus, the proposed system is not applicable in miss-match model Medikonda et al (2020) Twofold dataset…”

Section: Authentication Using Keystroke Dynamicsmentioning

confidence: 99%

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A broad review on non-intrusive active user authentication in biometrics

Thomas¹,

Mathew

2021

J Ambient Intell Human Comput

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Authentication is the process of keeping the user's personal information as confidential in digital applications. Moreover, the user authentication process in the digital platform is employed to verify the own users by some authentication methods like biometrics, voice recognition, and so on. Traditionally, a one-time login based credential verification method was utilized for user authentication. Recently, several new approaches were proposed to enhance the user authentication framework but those approaches have been found inconsistent during the authentication execution process. Hence, the main motive of this review article is to analyze the advantage and disadvantages of authentication systems such as voice recognition, keystroke, and mouse dynamics. These authentication models are evaluated in a continuous non-user authentication environment and their results have been presented in way of tabular and graphical representation. Also, the common merits and demerits of the discussed authentication systems are broadly explained discussion section. Henceforth, this study will help the researchers to adopt the best suitable method at each stage to build an authentication framework for non-intrusive active authentication.

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Section: Authentication Using Keystroke Dynamicsmentioning

confidence: 99%

“…The computation complexity of i-vector methods can be reduced using dimensionality reduction (Medikonda et al 2020). Even it has only achieved 15-30% accuracy under various simulated noisy conditions.…”

Section: Voice Biometric Recognitionmentioning

confidence: 99%

A broad review on non-intrusive active user authentication in biometrics

Thomas¹,

Mathew

2021

J Ambient Intell Human Comput

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Text-Independent Speaker Recognition System Using Feature-Level Fusion for Audio Databases of Various Sizes

Chauhan

Isshiki

2023

SN COMPUT. SCI.

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To improve the speaker recognition rate, we propose a speaker recognition model based on the fusion of different kinds of speech features. A new type of feature aggregation methodology with a total of 18 features is proposed and includes mel frequency cepstral coefficient (MFCC), linear predictive coding (LPC), perceptual linear prediction (PLP), root mean square (RMS), centroid, and entropy features along with their delta (Δ) and delta–delta (ΔΔ) feature vectors. The proposed approach is tested on five different sizes of speech datasets, namely the NIST-2008, voxforge, ELSDSR, VCTK, and voxceleb1 speech corpora. The results are evaluated using the MATLAB classification learner application with the linear discriminant (LD), K nearest neighbor (KNN), and ensemble classifiers. For the NIST-2008 and voxforge datasets, the best SI accuracy of 96.9% and 100% and the lowest speaker verification (SV) equal error rate (EER) values of 0.2% and 0% are achieved with the LD and KNN classifiers, respectively. For the VCTK and ELSDSR datasets, the best SI accuracy of 100% and the lowest SV EER of 0% are achieved with all three classifiers using different feature-level fusion approaches, while the highest SI accuracy and lowest EER achieved on the voxceleb1 database are 90% and 4.07%, respectively, using the KNN classifier. From the experimental results, it is observed that the fusion of different features with their delta and delta–delta values shows an increase in speaker identification accuracy of 10–50%, and the EER value for SV is reduced compared to the value obtained with a single feature.

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Information set supported deep learning architectures for improving noisy image classification

Bhardwaj

Wang

et al. 2023

Sci Rep

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Deep learning models have been widely used in many supervised learning applications. However, these models suffer from overfitting due to various types of uncertainty with deteriorating performance when facing data biases, class imbalance, or noise propagation. The Information-Set Deep learning (ISDL) architectures with four variants are developed by integrating information set theory and deep learning principles to address the critical problem of the absence of robust deep learning models. There is a description of the ISDL architectures, learning algorithms, and analytic workflows. The performance of the ISDL models and standard architectures is evaluated using a noise-corrupted benchmark dataset. The experimental results show that the ISDL models can efficiently handle noise-dominated uncertainty and outperform peer architectures.

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An information set-based robust text-independent speaker authentication

Cited by 4 publications

References 35 publications

A broad review on non-intrusive active user authentication in biometrics

A broad review on non-intrusive active user authentication in biometrics

Text-Independent Speaker Recognition System Using Feature-Level Fusion for Audio Databases of Various Sizes

Information set supported deep learning architectures for improving noisy image classification

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