On Reference Signal Estimation from Noisy Speech Using Deep Learning for Intelligibility Estimation
Hiroto Takahashi, Kazuhiro Kondo · 2018
We have been studying objective evaluation methods of speech intelligibility. In order to use the full reference method, which can estimate the speech quality with high accuracy, we propose a method to estimate the clean speech, which becomes the reference signal, from the noisy speech by noise removal. We have constructed a Deep Neural Network to eliminate noise and evaluated its noise removal performance.