  Emotion recognition has become a popular topic of interest, especially in the
field of human computer interaction. Previous works involve unimodal analysis
of emotion, while recent efforts focus on multi-modal emotion recognition from
vision and speech. In this paper, we propose a new method of learning about the
hidden representations between just speech and text data using convolutional
attention networks. Compared to the shallow model which employs simple
concatenation of feature vectors, the proposed attention model performs much
better in classifying emotion from speech and text data contained in the
CMU-MOSEI dataset.
