COVID-19 detection based on respiratory sensing from speech

COVID-19 affects a person's respiratory health, which is manifested in the form of shortness of breath during speech. Recent work shows that it is possible to use deep learning techniques to sense the speaker's respiratory parameters from a speech signal directly. Thus respiratory parameters like speech breathing rate and tidal volume can be computed and compared using deep learning techniques to detect COVID-19 from speech recordings. In this paper, we compute respiratory parameters using our pre-trained deep learning-based speech breathing models and use them for detecting COVID-19 from speech. Apart from using speech breathing models, we perform acoustic features identification using a statistical approach and classification based on low-level descriptive features. Our analysis investigates the distinction of speech of a healthy person and COVID-19 affected person. Copyright © 2022 ISCA.

A comparison of acoustic and linguistics methodologies for Alzheimer's dementia recognition

In the light of the current COVID-19 pandemic, the need for remote digital health assessment tools is greater than ever. This statement is especially pertinent for elderly and vulnerable populations. In this regard, the INTERSPEECH 2020 Alzheimer's Dementia Recognition through Spontaneous Speech (ADReSS) Challenge offers competitors the opportunity to develop speech and language-based systems for the task of Alzheimer's Dementia (AD) recognition. The challenge data consists of speech recordings and their transcripts, the work presented herein is an assessment of different contemporary approaches on these modalities. Specifically, we compared a hierarchical neural network with an attention mechanism trained on linguistic features with three acoustic-based systems: (i) Bag-of-Audio-Words (BoAW) quantising different low-level descriptors, (ii) a Siamese Network trained on log-Mel spectrograms, and (iii) a Convolutional Neural Network (CNN) end-to-end system trained on raw waveforms. Key results indicate the strength of the linguistic approach over the acoustics systems. Our strongest test-set result was achieved using a late fusion combination of BoAW, End-to-End CNN, and hierarchical-attention networks, which outperformed the challenge baseline in both the classification and regression tasks. Copyright © 2020 ISCA