A posterior union model for improved robust speech recognition in nonstationary noise

This paper investigates speech recognition with partial feature corruption, assuming unknown, time-varying noise characteristics. We extend our previous probabilistic union model from a conditional-probability formulation to a posterior-probability formulation. The new formulation allows the order of the model to be optimized for every single frame, and therefore greatly enhances the capability of the model for dealing with nonstationary noise corruption. Experiments have been conducted on two databases: TIDigits with noise corruption and Aurora 2, to demonstrate the improved robustness for the new model. Examples are presented showing that the new model can co-exist with existing noise-reduction techniques to provide improved noise robustness.