Log-Sepctral Linear Regression Based on Voicing Cut-Off Frequency for Robust Speech Recognition

This paper proposes a maximum likelihood log-spectral linear regression algorithm based on voicing cut-off frequency for robust speech recognition, which converts the pre-trained acoustic model to the log-spectral domain by the inverse discrete cosine transform and ignores the high-frequency part of the training mean and variance. Then the testing mean and variance are obtained by the log-spectral linear regression and the linear regression parameters are estimated from small amounts of adaptive data using the expectation-maximization algorithm under the maximum likelihood criterion. The experimental results show that the proposed algorithm can obtain more accurate testing acoustic models and outperforms the traditional linear regression method.