Multi-Modal Emotion recognition on IEMOCAP Dataset using Deep Learning
Published in
(2018)External Links:
TL
TL;DR
This approach is the first that uses the multiple modes of data offered by IEMOCAP for a more robust and accurate emotion detection, and it is hoped that this approach will help improve the quality of emotion detection systems in the future.
Abstract
Emotion recognition has become an important field of research in Human Computer Interactions as we improve upon the techniques for modelling the various aspects of behaviour. With the advancement of technology our understanding of emotions are advancing, there is a growing need for automatic emotion recognition systems. One of the directions the research is heading is the use of Neural Networks which are adept at estimating complex functions that depend on a large number and diverse source of input data. In this paper we attempt to exploit this effectiveness of Neural networks to enable us to perform multimodal Emotion recognition on IEMOCAP dataset using data from Speech, Text, and Motion capture data from face expressions, rotation and hand movements. Prior research has concentrated on Emotion detection from Speech on the IEMOCAP dataset, but our approach is the first that uses the multiple modes of data offered by IEMOCAP for a more robust and accurate emotion detection.
References15 items
1
Multimodal Sentiment Analysis: Addressing Key Issues and Setting Up the Baselines
2
Reusing Neural Speech Representations for Auditory Emotion Recognition
3
Context-Dependent Sentiment Analysis in User-Generated Videos
4
Attentive Convolutional Neural Network Based Speech Emotion Recognition: A Study on the Impact of Input Features, Signal Length, and Acted Speech
5