An efficient text-independent speaker verification for short utterance data from Mobile devices

Arora, Sanghamitra V.; Vig, Rekha

doi:10.1007/s11042-019-08196-7

Cited by 2 publications

(2 citation statements)

References 35 publications

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“…Arora &Vig [23] proposed effective SV for voice signal from cellular systems with the aim of extracting, characterizing and verifying the data about speaker identity. This system has four phases such as utterance splitting, feature extraction, feature choice and verification.…”

Section: Literature Surveymentioning

confidence: 99%

A Novel Optimized Deep Learner with Lightweighted Encryption-Based Speaker Verification

Rathinaraja¹,

Chandra²

2021

Preprint

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In the modern digitalized world, Speaker verification (SV) system is essential for authorizing the client’s credentials. To design an effective SV system, MGWOVSW-CAES-GMM system has been proposed. In this system, the Modified Grey Wolf Optimization (MGWO) technique was employed to optimize the variable sliding window size, FMPM features and training variables. The optimized features were watermarked and encrypted using a Chaotic-based Advanced Encryption Standard (CAES). Once the encryption process was completed, the encrypted features were forwarded to the recipient who executes the decryption and de-watermarking processes. At last, the decrypted features were classified using Gaussian Mixture Model (GMM) classifier. Conversely, MGWO has poor convergence rate and ineffective searching results. Hence, this article proposes an EEHOVSW-CAES-GMM system in which Enhanced Elephant Herding Optimization (EEHO) algorithm is applied instead of MGWO. On the contrary, the computational complexity of GMM classifier is high and its efficiency is less while increasing the number of features. For this reason, a Deep Neural Network (DNN) classifier is employed instead of GMM for recognizing the decrypted features and authorize the speaker’s identity. Besides, the parameters utilized in DNN topology are optimized using two different systems such as MGWOVSW-CAES-DNN and EEHOVSW-CAES-DNN for reducing the computational complexity and increasing the classification accuracy effectively when using more number of features. By using these classifiers, the speaker’s identity is verified and the attacks during the transmission are prevented with the highest security level.

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Section: Literature Surveymentioning

confidence: 99%

A Novel Optimized Deep Learner with Lightweighted Encryption-Based Speaker Verification

Rathinaraja¹,

Chandra²

2021

Preprint

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“…Many speech-related domains have already used feature selection, and the results have been positive (Prasad et al, 2007;Ellis and Bilmes, 2000;Chakraborty and Saha, 2010). As shown in Table 1 variety of FS approaches are studied in the literature for SV tasks, including dynamic programming, mutual information, and information gain (Pandit and Kittler, 1998;Cohen and Zigel, 2002;Saranya et al, 2017) and different metaheuristic algorithms like the Genetic Algorithm (GA) (Raymer et al, 2000;Day and Nandi, 2006), Particle Swarm Optimization (Kennedy and Eberhart, 1995;Nemati and Basiri, 2010), Ant Colony Optimization (ACO) (Dorigo et al, 2006;Nemati et al, 2008;Arora and Vig, 2020), Crow Search Algorithm (CSA) (Askarzadeh, 2016).…”

Section: Introductionmentioning

confidence: 99%

Statistically Significant Duration-Independent-based Noise-Robust Speaker Verification

Nirmal,

Jayaswal,

Kachare

2024

Int. j. math. eng. manag. sci.

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A speaker verification system models individual speakers using different speech features to improve their robustness. However, redundant features degrade the system's performance. This paper presents Statistically Significant Duration-Independent Mel frequency Cepstral Coefficients (SSDI-MFCC) features with the Extreme Gradient Boost classifier for improving the noise-robustness of speaker models. Eight statistical descriptors are used to generate signal duration-independent features, and a statistically significant feature subset is obtained using a t-test. A redeveloped Librispeech database by adding noises from the AURORA database to simulate real-world test conditions for speaker verification is used for evaluation. The SSDI-MFCC is compared with Principal Component Analysis (PCA) and Genetic Algorithm (GA). The comparative results showed average equal error rate improvements by 4.93 % and 3.48 % with the SSDI-MFCC than GA-MFCC and PCA-MFCC in clean and noisy conditions, respectively. A significant reduction in verification time is observed using SSDI-MFCC than the complete feature set.

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An efficient text-independent speaker verification for short utterance data from Mobile devices

Cited by 2 publications

References 35 publications

A Novel Optimized Deep Learner with Lightweighted Encryption-Based Speaker Verification

A Novel Optimized Deep Learner with Lightweighted Encryption-Based Speaker Verification

Statistically Significant Duration-Independent-based Noise-Robust Speaker Verification

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