  The INTERSPEECH 2020 Deep Noise Suppression Challenge is intended to promote
collaborative research in real-time single-channel Speech Enhancement aimed to
maximize the subjective (perceptual) quality of the enhanced speech. A typical
approach to evaluate the noise suppression methods is to use objective metrics
on the test set obtained by splitting the original dataset. Many publications
report reasonable performance on the synthetic test set drawn from the same
distribution as that of the training set. However, often the model performance
degrades significantly on real recordings. Also, most of the conventional
objective metrics do not correlate well with subjective tests and lab
subjective tests are not scalable for a large test set. In this challenge, we
open-source a large clean speech and noise corpus for training the noise
suppression models and a representative test set to real-world scenarios
consisting of both synthetic and real recordings. We also open source an online
subjective test framework based on ITU-T P.808 for researchers to quickly test
their developments. The winners of this challenge will be selected based on
subjective evaluation on a representative test set using P.808 framework.
