Speech Enhancement Based on Deep Neural Networks Considering Features of Speech Distribution
Tominaga Naoki, Yosuke Sugiura, Tetsuya Shimamura · Journal of Signal Processing · 2020
In this paper, we propose a new training architecture for speech enhancement based on deep neural networks.In the proposed architecture, the generative model producing the noiseless speech is trained so as to minimize the difference between two statistical distribution parameters of the clean speech and generated speech.From the experimental results, we verify that the proposed method can provide better results than the conventional method.