Linear Dynamic Models With Mixture of Experts Architecture for Recognition of Speech Under Additive Noise Conditions

This letter presents a new approach to enhance speech feature estimation in the log spectral domain under noisy environments. A mixture of linear dynamic models with an architecture similar to the so-called mixture of experts (ME) is investigated to describe the clean speech feature distribution parametrically. Switching Kalman filters are adapted to the proposed model, and they estimate the clean speech components by means of a generalized pseudo-Bayesian (GPB) algorithm. Experimental results suggest that compared with previous methods, the proposed approach can be more powerful to compensate the noisy speech features for robust speech recognition