关键词: 特征提取, 说话人识别, 伽马啁啾滤波器, 高斯混合模型-通用背景模型（GMM-UBM）, 辨识向量 ,  ,  , （i-vector）, 概率线性判别分析（PLDA）

Abstract: To solve the problem of poor speaker recognition robustness with power normalized cepstral coefficients (PNCC) feature in low-SNR noisy environment, this paper presents the anti-noise speech feature extraction algorithm for nonlinear power-function Gammachirp frequency cepstral coefficients (NPGFCC). Compared with PNCC, NPGFCC uses normalized compression Gammachirp filter bank which conforms to human auditory characteristics to replace Gammatone filter bank for the filtering and also combines piecewise nonlinear power-function transformation in characteristic parameters. This algorithm also adopts mean variance normalization and time series filtering methods to further improve its robustness in noisy environment, which is tested with an improved i-vector + PLDA model. The result shows that compared with currently common speech feature extraction algorithms, NPGFCC has the best anti-noise performance against different noises and different SNRs. Especially in the case of low SNRs, the NPGFCC feature has greater advantage over other speech features.

Key words: feature extraction, speaker recognition, Gammachirp filter, Gaussian mixture model-universal background model (GMM-UBM), identity-vector (i-vector), probabilistic linear discriminant analysis (PLDA)