Feature sparsity analysis for i-vector based speaker verification

Wei Li, Tianfan Fu, Hanxu You, Jie Zhu, Ning Chen

Abstract: Highlights•Feature (speaker frames) sparsity can lead to the over-fitting problem of i-vector based speaker verification system.•A series of designated experiments are performed to verify that feature sparsity makes the training of speaker model get stuck into local maxima.•An improved algorithm named adaptive first order Baum–Welch statistics analysis (AFSA) is proposed to compensate feature sparsity problem.•Experimental results show that AFSA can improve the performance of i-vector system especially when the durations of training and test utterances are short.