Speaker verification with multi-run ICA based speech enhancement

Forensic speaker verification systems show severe performance degradation in the presence of noise when the signal to noise ratio (SNR) is low. A possible solution to this problem is the use of multi-run independent component analysis (ICA) to reduce the effect of noise from the noisy speech signals. Previous works have used multi-run ICA in biosignal application; however, the effectiveness of multi-run ICA on noisy speaker verification has not been investigated yet. In this paper, we use multi-run ICA to enhance the noisy speech signals by choosing the highest signal to interference ratio (SIR) of the mixing matrix from different mixing matrices generated by iterating the fast ICA algorithm for several times. We use a combination of feature-warped mel frequency cepstral coefficients (MFCCs) and feature-warped MFCC extracted from the discrete wavelet transform (DWT) of the enhanced speech signals as the feature extraction. A state-of-the-art identity vector (i-vector) probabilistic linear discriminant analysis (PLDA) was used as a classifier in this paper. Experimental results demonstrate that the proposed method with multi-run ICA achieves high improvements in equal error rate (EER) of 66.68%, 69.24% and 70.78% over the baseline noisy speaker verification system, when the test speech signals are corrupted with CAR, STREET, and HOME noises respectively at −10 dB SNR.