The INTERSPEECH 2020 Deep Noise Suppression Challenge: Datasets, Subjective Testing Framework, and Challenge Results
Chandan K. A. Reddy, Vishak Gopal, Ross Cutler, Ebrahim Beyrami, Roger, Cheng, Harishchandra Dubey, Sergiy Matusevych, Robert Aichner, Ashkan Aazami,, Sebastian Braun, Puneet Rana, Sriram Srinivasan, Johannes Gehrke

TL;DR
The INTERSPEECH 2020 DNS Challenge aimed to advance real-time single-channel speech enhancement by providing datasets, a subjective testing framework, and analyzing results to improve noise suppression methods in real-world scenarios.
Contribution
This paper introduces open-source datasets, a subjective testing framework, and benchmark results for real-time speech enhancement, fostering collaborative research and addressing real-world noise conditions.
Findings
Models perform well on synthetic data but degrade on real recordings.
Objective metrics often do not align with subjective quality assessments.
Open datasets and testing frameworks facilitate better research and evaluation.
Abstract
The INTERSPEECH 2020 Deep Noise Suppression (DNS) 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. While the performance is good on the synthetic test set, 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-sourced 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…
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