Model Adaptation Algorithm Based on Central Subband Regression for Robust Speech Recognition

This paper proposes a model adaptation algorithm based on central sub band regression for robust speech recognition, which uses a linear transformation to approximate the relationship between the training and testing conditions for each channel of the Mel filter bank and its adjacent channels. The maximum likelihood estimation of each channel transform is obtained by several different divisions of all the Mel channels and sub-band adaptation. The experimental results show that the proposed algorithm can obtain more accurate testing acoustic models for rapid model adaptation and outperforms the traditional sub-band regression method.