Gammachirp Filter Banks Applied in Roust Speaker Recognition Based on GMM-UBM Classifier
Lei Deng and Yong Gao
College of Electronics and Information Engineering, Sichuan University,
China
Abstract: In this paper, authors propose an auditory feature extraction algorithm in order to
improve the performance of the speaker recognition system in noisy
environments. In this auditory feature
extraction algorithm, the Gammachirp filter bank is adapted to simulate the
auditory model of human cochlea. In addition, the following three
techniques are applied: cube-root compression method, Relative Spectral
Filtering Technique (RASTA), and Cepstral Mean and Variance Normalization
algorithm (CMVN).Subsequently, based on the theory of Gaussian Mixes Model-Universal
Background Model (GMM-UBM), the simulated experiment was conducted. The experimental results implied
that speaker recognition systems with the new auditory feature has better
robustness and recognition performance compared to Mel-Frequency
Cepstral Coefficients (MFCC), Relative
Spectral-Perceptual Linear Predictive (RASTA-PLP),Cochlear Filter Cepstral Coefficients (CFCC) and
gammatone Frequency Cepstral Coefficeints (GFCC).
Keywords: Feature extraction, gammachirp
filter bank, RASTA, CMVN, GMM-UBM.
Received May 9, 2017; accepted June 19, 2019
https://doi.org/10.34028/iajit/17/2/4