Efficient Invariant Features for Sensor Variability Compensation in Speaker Recognition

Sensors

Abdennour Alimohad¹  Ahmed Bouridane² 

[1] Research Laboratory in Electrical Engineering and Automatic LREA, University of MEDEA, Ain D'heb, Medea 26000, Algeria;School of Computing, Engineering and Information Sciences, Northumbria University, Newcastle Upon Tyne NE2 1XE, UK; E-Mail:

关键词: speaker recognition;    invariant features;    MFCCs;    GMM-UBM;    sensor variability;    DET curve;   

DOI  :  10.3390/s141019007

来源: mdpi

【 摘 要 】

In this paper, we investigate the use of invariant features for speaker recognition. Owing to their characteristics, these features are introduced to cope with the difficult and challenging problem of sensor variability and the source of performance degradation inherent in speaker recognition systems. Our experiments show: (1) the effectiveness of these features in match cases; (2) the benefit of combining these features with the mel frequency cepstral coefficients to exploit their discrimination power under uncontrolled conditions (mismatch cases). Consequently, the proposed invariant features result in a performance improvement as demonstrated by a reduction in the equal error rate and the minimum decision cost function compared to the GMM-UBM speaker recognition systems based on MFCC features.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland.