SINGLE CHANNEL JOINT SPEECH DEREVERBERATION AND DENOISING USING DEEP PRIORS

Abstract

Single channel speech de-reverberation and de-noising is a challenging problem, since directional information is not available in a single channel when compared to multi-channel approaches. Several deep neural network (DNN) based solutions have been proposed in the recent past to solve this problem. These solutions are sequential and de-reverberate the signal after denoising. Additionally these solutions have not utilized the maximum a posteriori (MAP) method which requires the knowledge of the prior. In this work a MAP method is proposed to solve the speech de-reverberation and de-noising problem jointly. A half quadratic splitting (HQS) method is used to solve the joint MAP problem in a DNN framework by splitting it into two minimization problems. The deep prior is modeled using a latent variable and obtained using an iterative method. The performance of the proposed method is illustrated using subjective and objective measures. Experiments on continuous speech recognition are also used to demonstrate the significance of this method.