Multi-Modal Emotion recognition on IEMOCAP Dataset using Deep Learning (2018-04-16T00:00:00.000000Z)

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.

Authors

Samarth Tripathi

1 papers

Sarthak Tripathi

1 papers

Homayoon Beigi

1 papers

Multi-Modal Emotion recognition on IEMOCAP Dataset using Deep Learning

TL;DR

Abstract

Authors

References15 items

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FUNDAMENTALS OF SPEAKER RECOGNITION

IEMOCAP: interactive emotional dyadic motion capture database

Emotion recognition: the role of facial movement and the relative importance of upper and lower areas of the face.

A Multimodal Database for Affect Recognition and Implicit Tagging

Field of Study