Robust Hybrid Features Based Text Independent Speaker Identification System over Noisy Additive Channel

  • Ali Muayad Jalil Computer Engineering Department, Mustansiriyah University, Baghdad, Iraq

Abstract

Robustness of speaker identification systems over additive noise is crucial for real-world applications.  In this paper, two robust features named Power Normalized Cepstral Coefficients (PNCC) and Gammatone Frequency Cepstral Coefficients (GFCC) are combined together to improve the robustness of speaker identification system over different types of noise.  Universal Background Model Gaussian Mixture Model (UBM-GMM) is used as a feature matching and a classifier to identify the claim speakers. Evaluation results show that the proposed hybrid feature improves the performance of identification system when compared to conventional features over most types of noise and different signal-to-noise ratios.

Published
2020-08-27
How to Cite
JALIL, Ali Muayad. Robust Hybrid Features Based Text Independent Speaker Identification System over Noisy Additive Channel. JOURNAL OF ENGINEERING AND SUSTAINABLE DEVELOPMENT, [S.l.], v. 24, n. 4, p. 56-70, aug. 2020. ISSN 2520-0925. Available at: <http://jeasd.uomustansiriyah.edu.iq/index.php/jeasd/article/view/827>. Date accessed: 22 oct. 2020.