Robust Text-Independent Speaker Identification and Verification Using Multi-Feature Fusion and Student’s t Modelling

Authors

  • Musab Tahseen Salahaldeen Al-Kaltakchi Mustansiriyah University,Department of Electrical Engineering, College of Engineering, Baghdad https://orcid.org/0000-0001-5542-9144
  • Mohanad Abd Shehab Mustansiriyah University,Department of Electrical Engineering, College of Engineering, Baghdad
  • Emad A. Hussien Mustansiriyah University,Department of Electrical Engineering, College of Engineering, Baghdad
  • Amal Ibrahim Nasser Mustansiriyah University,Department of Electrical Engineering, College of Engineering, Baghdad

Abstract

Abstract—This paper presents a text-independent speaker identification system that utilizes MFCC, LPC, prosody, and optimized multi-level DWT features for robust speaker modeling. The system is designed for multiple standard speech databases, including TIMIT, NTIMIT, SITW, and NIST2008. During train- ing, features from each speaker are normalized to zero-mean and unit-variance, and Student-t distributions are fitted to model the statistical characteristics of each speaker. For testing, features are normalized using the corresponding speaker’s training statistics, and speaker identity is predicted based on maximum log-likelihood estimation over the trained models. Experimental results confirm the superiority of the proposed system, which achieves high accuracy across multiple datasets (e.g., 98.33% on TIMIT, 89.38% on NTIMIT, 96.88% on SITW, and 100.00% on NIST2008) and consistently outperforms existing state-of-the- art methods under AWGN conditions, demonstrating significant improvements in identification accuracy and the effectiveness of multi-feature fusion and Student-t modeling.

Author Biography

Musab Tahseen Salahaldeen Al-Kaltakchi, Mustansiriyah University,Department of Electrical Engineering, College of Engineering, Baghdad

Dr. Musab T. S. Al-Kaltakchi is a lecturer in the Electrical Engineering Department, Mustansiriyah University, Baghdad-Iraq. He obtained his BSc in Electrical Engineering in 1996 and obtained his MSc in Communication and Electronics in 2004 from Mustansiriyah University. He was awarded a PhD degree in Electrical Engineering/ Digital Signal Processing from Newcastle University, UK in 2018. He is a member of the Institute of Electrical and Electronic Engineering (IEEE) and also in the Institute of Engineering and Technology (IET). His research interests include Speaker identification and verification, Speech and audio signal processing, Machine learning, Artificial intelligence, Pattern recognition, and Biometrics. He can be contacted at Email: musab.tahseen@gmail.com & at Email: m.t.s.al_kaltakchi@uomustansiriyah.edu.iq.

Additional Files

Published

2026-05-16

Issue

Vol. 72 No. 2 (2026)

Section

Digital Signal Processing

License

Copyright (c) 2026 International Journal of Electronics and Telecommunication

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