  This paper introduces our approaches for the Mask and Breathing Sub-Challenge
in the Interspeech COMPARE Challenge 2020. For the mask detection task, we
train deep convolutional neural networks with filter-bank energies,
gender-aware features, and speaker-aware features. Support Vector Machines
follows as the back-end classifiers for binary prediction on the extracted deep
embeddings. Several data augmentation schemes are used to increase the quantity
of training data and improve our models' robustness, including speed
perturbation, SpecAugment, and random erasing. For the speech breath monitoring
task, we investigate different bottleneck features based on the Bi-LSTM
structure. Experimental results show that our proposed methods outperform the
baselines and achieve 0.746 PCC and 78.8% UAR on the Breathing and Mask
evaluation set, respectively.
