Power Normalized Gammachirp Cepstral (PNGC) coefficients-based approach for robust speaker recognition

Abstract

Speaker identification or recognition task aims to identify persons from their voices. This paper introduces a new feature extraction approach for robust speaker recognition named Power Normalized Gammachirp Cepstral (PNGC). Key aspect of our method is the use of a biologically motivated auditory perceptual model. For speaker modeling, we use the Gaussian Mixture Model-Universal Background Model (GMM-UBM). The proposed approach includes the following main processing steps: (1) an efficient auditory filter model based on the normalized Gammachirp auditory Filterbank to estimate the cochlea spectral behavior, (2) an environmental noise compensation bloc that employs: a medium-time power analysis, an asymmetric noise-suppression module with a temporal masking module, and a frequency smoothing module to compensate the environmental noise effects, and (3) a power-law nonlinearity. Conducted experimentations on TIMIT and Aurora datasets proved that our proposed PNGC approach achieves an improved recognition accuracy compared to the MFCC and recent PNCC methods for noisy speaker recognition.