A Deep Neural Network Based Kalman Filter for Time Domain Speech Enhancement

In this paper, we present a novel deep neural network (DNN) based Kalman filter (KF) algorithm for speech enhancement, where DNN is applied for estimating key parameters in the KF, namely, the linear prediction coefficients (LPCs). By training the DNN with a large database and making use of the powerful learning ability of DNN, our proposed DNN-KF algorithm is able to estimate LPCs from noisy speech more accurately and robustly, leading to an improved performance as compared to traditional KF based approaches in speech enhancement. Experimental results demonstrate that our DNN-KF method outperforms two existing KF based speech enhancement methods in terms of both speech quality and intelligibility.