Running summarizing tools on a new article

Abstract: Speaker verification is a critical component of biometric security systems, enabling the authentication of a speaker’s identity regardless of spoken content. This study proposes a robust deep learning-based speaker verification framework designed to address challenges posed by real-world variability in language, speaking style, and recording conditions. The system is trained on a multi-variability speech dataset (IITG-MV/IMSV) collected from 100 speakers across five sensors, two languages (English and a native language), and both conversational and reading styles. Acoustic features including log-Mel spectrograms, MFCCs, and Chroma are extracted from preprocessed audio, which undergoes speech segmentation, noise reduction, and normalization. The architecture employs residual convolutional blocks, SpecAugment for data augmentation, and a lightweight transformer to capture speaker-discriminative patterns. Evaluation using Equal Error Rate (EER) demonstrates the model’s effectiveness across linguistic and sensor variations. The proposed system achieves a notable EER of 2.8% in English settings, outperforming the baseline 3.82%, and shows improved generalization in cross-lingual scenarios (EER of 4.7% vs. 5.01%). These results highlight the system’s potential for scalable and accurate speaker verification in diverse and multilingual environments.