On noise estimation for robust speech recognition using vector Taylor series

In this paper, we propose a novel noise variance estimation method using the fixed point method for the VTS-based robust speech recognition. Noise parameters are re-estimated over a given utterance using an EM algorithm. The derivative of the auxiliary function with respect to the noise variance is resolved, and the fixed point algorithm estimates the noise variance by recursively approximating the root of the resulting derivative. The method leads to a re-estimation formula with a flavor like the standard ML variance estimation, and the iteration procedure is step-size free. We also investigate improving the noise estimation for efficient VTS adaptation. Several fast noise estimation methods are examined including estimation from non-speech areas and incremental adaptation. In the evaluation over Aurora 2 database, the proposed noise variance estimation method obtains a significant improvement in recognition accuracy over the method using sample variance. Further experiments show that the VTS ML estimation over non-speech areas is an effective fast adaptation method. The final refined approach achieves 8.75% WER, 13% relative improvement over the conventional VTS adaptation.