End-to-End Multimodal Emotion Recognition Using Deep Neural Networks

TLDR

Automatic affect recognition is challenging because emotions are expressed through multiple modalities, and while deep neural networks have recently achieved success, applications span multimedia retrieval and human‑computer interaction. The authors propose an end‑to‑end emotion recognition system that fuses auditory and visual modalities. They extract speech features with a CNN and visual features with a 50‑layer ResNet, then model temporal context and robustness to outliers using LSTM networks. On the RECOLA dataset of the AVEC 2016 challenge, the system outperforms traditional approaches based on handcrafted features for spontaneous and natural emotion prediction.

Abstract

Automatic affect recognition is a challenging task due to the various modalities emotions can be expressed with. Applications can be found in many domains including multimedia retrieval and human computer interaction. In recent years, deep neural networks have been used with great success in determining emotional states. Inspired by this success, we propose an emotion recognition system using auditory and visual modalities. To capture the emotional content for various styles of speaking, robust features need to be extracted. To this purpose, we utilize a Convolutional Neural Network (CNN) to extract features from the speech, while for the visual modality a deep residual network (ResNet) of 50 layers. In addition to the importance of feature extraction, a machine learning algorithm needs also to be insensitive to outliers while being able to model the context. To tackle this problem, Long Short-Term Memory (LSTM) networks are utilized. The system is then trained in an end-to-end fashion where - by also taking advantage of the correlations of the each of the streams - we manage to significantly outperform the traditional approaches based on auditory and visual handcrafted features for the prediction of spontaneous and natural emotions on the RECOLA database of the AVEC 2016 research challenge on emotion recognition.