Title: Robust speaker recognition based on biologically inspired features

Authors: Youssef Zouhir; Ines Ben Fredj; Kaïs Ouni; Mohamed Zarka

Addresses: Research Laboratory Smart Electricity & ICT, SE&ICT Lab., LR18ES44, National Engineering School of Carthage, University of Carthage, Tunis, 2035, Tunisia ' Research Laboratory Smart Electricity & ICT, SE&ICT Lab., LR18ES44, National Engineering School of Carthage, University of Carthage, Tunis, 2035, Tunisia ' Research Laboratory Smart Electricity & ICT, SE&ICT Lab., LR18ES44, National Engineering School of Carthage, University of Carthage, Tunis, 2035, Tunisia ' Computer Science Department, College of Science and Arts in Tanumah, King Khalid University, Abha 61421, Saudi Arabia

Abstract: This paper proposes two speech parameterisation techniques for noise-robust speaker recognition: the normalised gammachirp cepstral coefficients (NGCC) and the perceptual linear predictive normalised gammachirp (PLPnGc). These techniques employ a biologically inspired auditory model that simulates the cochlea spectral behaviour. In an automatic speaker recognition (ASR) system, we consider the Gaussian mixture model-universal background model (GMM-UBM) for speaker modelling. The performances are evaluated in clean and noisy environments using Timit, Aurora, and Demand databases. The experimental results in noisy environments showed that the biologically inspired feature extraction techniques give a better recognition rate than state-of-the-art methods.

Keywords: auditory filter model; biologically inspired features; NGCC; normalised gammachirp cepstral coefficients; PLPnGc; perceptual linear predictive normalised gammachirp; GMM-UBM; Gaussian mixture model-universal background model; robust speaker recognition.

DOI: 10.1504/IJSISE.2020.113559

International Journal of Signal and Imaging Systems Engineering, 2020 Vol.12 No.1/2, pp.19 - 27

Accepted: 05 Apr 2020
Published online: 11 Mar 2021 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article