Mask Detection and Breath Monitoring from Speech: on Data Augmentation, Feature Representation and Modeling

2020/08/12 by Haiwei Wu, Wu, Haiwei, Lin Zhang +11
Computer Science · Medicine · #Audio and Speech Processing (eess.AS) #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Sound (cs.SD) #Speech Recognition and Synthesis #Speech and Audio Processing #Voice and Speech Disorders #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2008.05175

openalex publication_date 2020/08/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• Recognising realistic emotions and affect in speech: State of the art and lessons learnt from the first challenge
• auDeep: Unsupervised Learning of Representations from Audio with Deep Recurrent Neural Networks
• Random Erasing Data Augmentation

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