Multimodal feature evaluation and fusion for emotional well-being monitorization

Abstract

Mental health is a global issue that plays an important roll in the overall well-being of a person. Because of this, it is important to preserve it, and conversational systems have proven to be helpful in this task. This research is framed in the MENHIR project, which aims at developing a conversational system for emotional well-being monitorization. As a first step for achieving this purpose, the goal of this paper is to select the features that can be helpful for training a model that aims to detect if a patient suffers from a mental illness. For that, we will use transcriptions extracted from conversational information gathered from people with different mental health conditions to create a data set. After the feature selection, the constructed data set will be fed to supervised learning algorithms and their performance will be evaluated. Concretely we will work with random forests, neural networks and BERT.