Joint Estimation of Noise and Channel Distortion in a Generalized EM Framework

The performance of speech cleaning and noise adaptation algorithms is heavily dependent on the quality of the noise and channel models. Various strategies have been proposed in the literature for adapting to the current noise and channel conditions. In this paper, we describe the joint learning of noise and channel distortion in a novel framework called ALGONQUIN. The learning algorithm employs a generalized EM strategy wherein the E step is approximate. We discuss the characteristics of the new algorithm, with a focus on convergence rates and parameter initialization. We show that the learning algorithm can successfully disentangle the non-linear effects of noise and linear effects of the channel and achieve a relative reduction in WER of 21.8% over the non-adaptive algorithm.