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.

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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

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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Frustration is a common response during game interactions, typically decreasing a user's engagement and leading to game failure. Artificially intelligent methods capable to automatically detect a user's level of frustration at an early stage are hence of great interest for game designers, since this would enable optimisation of a player's experience in real-time. Nevertheless, research in this context is still in its infancy, mainly relying on the use of pre-trained models and fine-tuning tailored to a specific dataset. Furthermore, this lack in research is due to the limited data available and to the ambiguous labelling of frustration, which leads to outcomes which are not generalisable in the real-world. Meanwhile, contrastive loss has been considered instead of the traditional cross-entropy loss in a variety of machine learning applications, showing to be more robust for system stability alternative in self-supervised learning. Following this trend, we hypothesise that using a supervised contrastive loss might overcome the limitations of the cross-entropy loss yielded by the labels' ambiguity. In fact, our experiments demonstrate that using the supervised contrastive method as a loss function, results improve for the automatic recognition (binary frustration vs nofrustration) of game-induced frustration from speech with an Unweighted Average Recall increase from 86.4 % to 89.9 %.