The Verbal and Non Verbal Signals of Depression -- Combining Acoustics, Text and Visuals for Estimating Depression Level

Depression is a serious medical condition that is suffered by a large number of people around the world. It significantly affects the way one feels, causing a persistent lowering of mood. In this paper, we propose a novel attention-based deep neural network which facilitates the fusion of various modalities. We use this network to regress the depression level. Acoustic, text and visual modalities have been used to train our proposed network. Various experiments have been carried out on the benchmark dataset, namely, Distress Analysis Interview Corpus - a Wizard of Oz (DAIC-WOZ). From the results, we empirically justify that the fusion of all three modalities helps in giving the most accurate estimation of depression level. Our proposed approach outperforms the state-of-the-art by 7.17% on root mean squared error (RMSE) and 8.08% on mean absolute error (MAE).Comment: 10 pages including references, 2 figure

Trends and Overview: The Potential of Conversational Agents in Digital Health

With the COVID-19 pandemic serving as a trigger, 2020 saw an unparalleled global expansion of tele-health [23]. Tele-health successfully lowers the need for in-person consultations and, thus, the danger of contracting a virus. While the COVID-19 pandemic sped up the adoption of virtual healthcare delivery in numerous nations, it also accelerated the creation of a wide range of other different technology-enabled systems and procedures for providing virtual healthcare to patients. Rightly so, the COVID-19 has brought many difficulties for patients (https://www.who.int/news/item/02-03-2022-covid-19-pandemic-triggers-25-increase-in-prevalence-of-anxiety-and-depression-worldwide ) who need continuing care and monitoring for mental health issues and/or other chronic diseases