Gammachirp Filter Banks Applied in Roust Speaker Recognition Based on GMM-UBM Classifier

摘要

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 exfraction 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 Cepsfral 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 Cepsfral Coefficeints (GFCC).