Asmita Nirmal
Department of Electronics and Telecommunication Engineering, Datta Meghe College of Engineering, Navi Mumbai, Maharashtra, India.

Deepak Jayaswal
Department of Electronics and Telecommunication Engineering, St. Francis Institute of Technology, Mumbai, Maharashtra, India.

Pramod H. Kachare
Department of Electronics and Telecommunication Engineering, Ramrao Adik Institute of Technology, Navi Mumbai, Maharashtra, India.

DOI https://doi.org/10.33889/IJMEMS.2024.9.1.008

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Abstract

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.

Keywords- Extreme gradient boost, Feature selection, Mel-frequency cepstral coefficients, Speaker verification.

Citation

Nirmal, A., Jayaswal, D., & Kachare, P. H (2024). Statistically Significant Duration-Independent-based Noise-Robust Speaker Verification. International Journal of Mathematical, Engineering and Management Sciences, 9(1), 147-162. https://doi.org/10.33889/IJMEMS.2024.9.1.008.