Feature Compensation using More Accurate Statistics of Modeling Error
Woohyung Lim, Jong Kyu Kim, Nam Soo Kim · 2007
In this paper, we propose a novel approach to feature compensation for robust speech recognition in noisy environments. We analyze the statistics of the modeling error in the log mel magnitude spectrum domain, and model it as a Gaussian distribution. The mean and variance of the distribution are Gaussian functions of the SNR, which enables us to use the SNR dependency of the modeling error efficiently. The proposed feature compensation approach, which is based on the interacting multiple model (IMM) technique, incorporates the statistics of the modeling error and shows significant improvement in the AURORA2 speech recognition task.