The INTERSPEECH 2020 Deep Noise Suppression Challenge: Datasets, Subjective Speech Quality and Testing Framework
This challenge tackles the issue of scalable and accurate evaluation of noise suppression models for researchers and practitioners in speech processing, though it is incremental as it builds on existing datasets and frameworks.
The INTERSPEECH 2020 Deep Noise Suppression Challenge addressed the problem of evaluating real-time single-channel speech enhancement methods by providing open-source datasets and a subjective testing framework to improve correlation with human perception, resulting in a competition where winners were selected based on subjective evaluations using the P.808 framework.
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.