Research on Speech Enhancement Based on Deep Neural Network
Yiting Wang, Zhenhua Wei · Journal of Physics Conference Series · 2020
Abstract The traditional single-channel speech enhancement algorithm has many unreasonable assumptions, which limit the performance of the algorithm. The speech enhancement based on deep neural network can effectively eliminate the problems such as “music noise” in the traditional methods, thus achieving better results than the traditional single-channel speech enhancement. However, the DNN-based speech enhancement model does not perform well at low SNR. In order to improve the robustness of the model, we used voice activity detection (VAD) to process the training data to obtain a new VAD-DNN speech enhancement model. We added 100 kinds of noise in the training set to improve the ability of the model to deal with the unseen noise. At the same time, in order to prevent the occurrence of overfitting problem, we introduced dropout technology to process the network, and improved the generalization ability of the model by using noise awareness training (NAT). Through experiments, we found that the speech enhancement model based on VAD-DNN improved PESQ index by 0.02 and STOI index by 0.01 on average under the condition of low SNR.