The COVID-19 outbreak was announced as a global pandemic by the World Health Organisation in March 2020 and has affected a growing number of people in the past few weeks. In this context, advanced artificial intelligence techniques are brought to the fore in responding to fight against and reduce the impact of this global health crisis. In this study, we focus on developing some potential use-cases of intelligent speech analysis for COVID-19 diagnosed patients. In particular, by analysing speech recordings from these patients, we construct audio-onlybased models to automatically categorise the health state of patients from four aspects, including the severity of illness, sleep quality, fatigue, and anxiety. For this purpose, two established acoustic feature sets and support vector machines are utilised. Our experiments show that an average accuracy of .69 obtained estimating the severity of illness, which is derived from the number of days in hospitalisation. We hope that this study can foster an extremely fast, low-cost, and convenient way to automatically detect the COVID-19 disease.

Every year, respiratory diseases affect millions of people worldwide, becoming one of the main causes of death in nowadays society. Currently, the COVID-19-known as a novel respiratory illness-has triggered a global health crisis, which has been identified as the greatest challenge of our time since the Second World War. COVID-19 and many other respiratory diseases present often common symptoms, which impairs their early diagnosis; thus, restricting their prevention and treatment. In this regard, in order to encourage a faster and more accurate detection of these kinds of diseases, the automatic identification of respiratory illness through the application of machine learning methods is a very promising area of research aimed to support clinicians. With this in mind, we apply attention-based Convolutional Neural Networks for the recognition of adventitious respiratory cycles on the International Conference on Biomedical Health Informatics 2017 challenge database. Experimental results indicate that the architecture of residual networks with attention mechanism achieves a significant improvement w. r. t. the baseline models.

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Computer audition (CA) has experienced a fast development in the past decades by leveraging advanced signal processing and machine learning techniques. In particular, for its non-invasive and ubiquitous character by nature, CA based applications in healthcare have increasingly attracted attention in recent years. During the tough time of the global crisis caused by the COVID-19 (coronavirus disease 2019), scientists and engineers in data science have collaborated to think of novel ways in prevention, diagnosis, treatment, tracking, and management of this global pandemic. On one hand, we have witnessed the power of 5G, internet of things, big data, computer vision, and artificial intelligence in applications of epidemiology modelling, drug and/or vaccine finding and designing, fast CT screening, and quarantine management. On the other hand, relevant studies in exploring the capacity of CA are extremely lacking and underestimated. To this end, we propose a novel multi-task speech corpus for COVID-19 research usage. We collected 51 confirmed COVID-19 patients' in-the-wild speech data in Wuhan city, China. We define three main tasks in this corpus, i. e., three-category classification tasks for evaluating the physical and/or mental

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The rapid emergence of COVID-19 has become a major public health threat around the world. Although early detection is crucial to reduce its spread, the existing diagnostic methods are still insufficient in bringing the pandemic under control. Thus, more sophisticated systems, able to easily identify the infection from a larger variety of symptoms, such as cough, are urgently needed. Deep learning models can indeed convey numerous signal features relevant to fight against the disease; yet, the performance of state-of-the-art approaches is still severely restricted by the feature information loss typically due to the high number of layers. To mitigate this phenomenon, identifying the most relevant feature areas by drawing into attention mechanisms becomes essential. In this paper, we introduce Spatial Attentive ConvLSTM-RNN (SACRNN), a novel algorithm that is using Convolutional Long-Short Term Memory Recurrent Neural Networks with embedded attention that has the ability to identify the most valuable features. The promising results achieved by the fusion between the proposed model and a conventional Attentive Convolutional Recurrent Neural Network, on the automatic recognition of COVID-19 coughing (73.2 % of Unweighted Average Recall) show the great potential of the presented approach in developing efficient solutions to defeat the pandemic.