Histogram Based Double Gaussian Feature Normalization For Robust Language Recognition

For automatic language recognition, performance can be seriously degraded due to the transfer characteristics of the communication channel. Many methods are proposed to compensate the effect of the environment for better recognition results. In this paper, we propose a histogram based double Gaussian feature normalization method for robust language recognition. Compared with the baseline system, the proposed method achieves a relative error reduction of 17.4%, which shows advantages over other common feature normalization methods in language recognition systems.