Robust speaker recognition based on biologically inspired features
by Youssef Zouhir; Ines Ben Fredj; Kaïs Ouni; Mohamed Zarka
International Journal of Signal and Imaging Systems Engineering (IJSISE), Vol. 12, No. 1/2, 2020

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.

Online publication date: Thu, 11-Mar-2021

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Signal and Imaging Systems Engineering (IJSISE):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com