Geometric Information Based Monaural Speech Separation Using Deep Neural Network

The performance of deep neural network (DNN) based monaural speech separation methods is limited in reverberant and noisy room environments. In this paper, we propose a new DNN training target which incorporates geometric information describing the target speaker and microphone to improve the performance in reverberant and noisy room environments. The experiments are based on the IEEE corpus and the NOISEX database and real impulse responses (RIRs). The objective evaluations, short-time objective intelligibility (STOI) and perceptual evaluation of speech quality (PESQ) confirm the efficiency of the proposed direct path ratio mask (DRM).