Abstract
This paper describes deep learning approaches for the Mask and Breathing Sub-Challenges (SCs), which are addressed by the INTERSPEECH 2020 Computational Paralinguistics Challenge. Motivated by outstanding performance of state-of-the-art end-to-end (E2E) approaches, we explore and compare effectiveness of different deep Convolutional Neural Network (CNN) architectures on raw data, log Mel-spectrograms,
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