Environment compensation based on maximum a posteriori estimation for improved speech recognition

In this paper, we describe environment compensation approach based on MAP (maximum a posteriori) estimation assuming that the noise can be modeled as a single Gaussian distribution. It employs the prior information of the noise to deal with environmental variabilities. The acoustic-distorted environment model in the cepstral domain is approximated by the truncated first-order vector Taylor series(VTS) expansion and the clean speech is trained by using Self-Organizing Map (SOM) neural network with the assumption that the speech can be well represented as the multivariate diagonal Gaussian mixtures model (GMM). With the reasonable environment model approximation and effective clustering for the clean model, the noise is well refined using batch-EM algorithm under MAP criterion. Experiment with large vocabulary speaker-independent continuous speech recognition shows that this approach achieves considerable improvement on recognition performance.