  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.
