Deep Neural Network based Supervised Speech Enhancement in Speech-Babble Noise

摘要

Speech enhancement is fundamental for many real-time speech applications and it is challenging in case of single-channel because practically only one data channel is available. Without any constraint, a countless range of solutions are possible to solve this problem. In this paper, we present a supervised learning approach to enhance a speech degraded by speech-babble noise, which is most challenging type of noise in speech enhancement systems. The proposed method is composed of deep neural networks (DNNs) and less aggressive Wiener filtering (LW) for speech enhancement, labeled as the DNN-LW. The proposed method is composed of the training and testing stages, respectively. The DNN in the training stage calculates the magnitude spectrums of noise-free speech and the noise signals, respectively from the input noise-masked speech features concurrently. The Less aggressive Wiener filter is then placed as an extra layer on top of the deep neural network to create the enhanced magnitude spectrum. Finally, the phase of noisy speech is used to restore the estimate of clean speech. During testing stage, the trained DNN is provided the features of noise-masked speech to attain the enhanced speech. The experimental results revealed that the DNN-LW approach performs significantly better against baseline speech enhancement methods.