Generative Adversarial Networks for Single Channel Separation of Convolutive mixed Speech Signals

Abstract The suppression of interference for speech recognition is of great significance in noisy situation, especially in single channel receiving mode, the suppression of interference is much more difficult. In this paper, we propose a generative adversarial network (GAN) based method for single channel dereverberation and speech separation. Different from the existing methods, our method considers the influence of strong reverberation on the observed signals. The proposed network involves two parts: reverberation suppression and target speech enhancement. Firstly, we use an improved CyclyGAN to compensate the multi-path effect on both target speech and interference. Secondly, we propose a differentialGAN to extract both target speech and interference while the interference enhancement network can indirectly improve the performance of target speech enhancement network. We use the real and imaginary parts of the complex spectrum as the feature vector, which avoids the phase mismatch during signal recovery. Simulation results show that our method is superior to its competitors in terms of multiple metrics in severe reverberation environment.