Deep learning our everyday emotions a short overview

Abstract

Emotion is omnipresent in our daily lives and has a significant influence on our functional activities. Thus, computer-based recognising and monitoring of affective cues can be of interest such as when interacting with intelligent systems, or for health-care and security reasons. In this light, this short overview focuses on audio/visual and textual cues as input feature modality for automatic emotion recognition. In particular, it shows how these can best be modelled in a Neural Network context. This includes deep learning, and sparse auto-encoders for transfer learning of a compact task and population representation. It further shows avenues towards massively autonomous rich multitask-learning and required confidence estimation as is needed to prepare such technology for real-life application.