Speech enhancement based on simple recurrent unit network

Abstract

Speech enhancement is a crucial and challenging task in many applications. A novel speech enhancement method based on the simple recurrent unit (SRU) is proposed in this paper. First, the log-power spectra of noisy and clean speeches are extracted. Then, the mapping relationship between noisy and clean speech spectra is learned by a multiple-layer stacked SRU network. Finally, the well-trained model is used to predict the corresponding clean speech spectra from the noisy speech spectra and the whole clean speech waveform can be recovered. Compared with the existing algorithms, DNN, LSTM and GRU, the proposed method achieves significant improvements at training speed and has capability to balance the performance and the training time. Experimental results demonstrate the validity and robustness of the proposed method.