AVEC 2016 – Depression, mood, and emotion recognition workshop and challenge

The Audio/Visual Emotion Challenge and Workshop (AVEC 2016) "Depression, Mood and Emotion" will be the sixth competition event aimed at comparison of multimedia processing and machine learning methods for automatic audio, visual and physiological depression and emotion analysis, with all participants competing under strictly the same conditions. The goal of the Challenge is to provide a common benchmark test set for multi-modal information processing and to bring together the depression and emotion recognition communities, as well as the audio, video and physiological processing communities, to compare the relative merits of the various approaches to depression and emotion recognition under well-defined and strictly comparable conditions and establish to what extent fusion of the approaches is possible and beneficial. This paper presents the challenge guidelines, the common data used, and the performance of the baseline system on the two tasks

AVEC 2019 workshop and challenge: state-of-mind, detecting depression with AI, and cross-cultural affect recognition

The Audio/Visual Emotion Challenge and Workshop (AVEC 2019) "State-of-Mind, Detecting Depression with AI, and Cross-cultural Affect Recognition" is the ninth competition event aimed at the comparison of multimedia processing and machine learning methods for automatic audiovisual health and emotion analysis, with all participants competing strictly under the same conditions. The goal of the Challenge is to provide a common benchmark test set for multimodal information processing and to bring together the health and emotion recognition communities, as well as the audiovisual processing communities, to compare the relative merits of various approaches to health and emotion recognition from real-life data. This paper presents the major novelties introduced this year, the challenge guidelines, the data used, and the performance of the baseline systems on the three proposed tasks: state-of-mind recognition, depression assessment with AI, and cross-cultural affect sensing, respectively

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

Intelligent Advanced User Interfaces for Monitoring Mental Health Wellbeing

It has become pressing to develop objective and automatic measurements integrated in intelligent diagnostic tools for detecting and monitoring depressive states and enabling an increased precision of diagnoses and clinical decision-makings. The challenge is to exploit behavioral and physiological biomarkers and develop Artificial Intelligent (AI) models able to extract information from a complex combination of signals considered key symptoms. The proposed AI models should be able to help clinicians to rapidly formulate accurate diagnoses and suggest personalized intervention plans ranging from coaching activities (exploiting for example serious games), support networks (via chats, or social networks), and alerts to caregivers, doctors, and care control centers, reducing the considerable burden on national health care institutions in terms of medical, and social costs associated to depression cares