A Novel Attention-Based Gated Recurrent Unit and its Efficacy in Speech Emotion Recognition

Abstract

Notwithstanding the significant advancements in the field of deep learning, the basic long short-term memory (LSTM) or Gated Recurrent Unit (GRU) units have largely remained unchanged and unexplored. There are several possibilities in advancing the state-of-art by rightly adapting and enhancing the various elements of these units. Activation functions are one such key element. In this work, we explore using diverse activation functions within GRU and bi-directional GRU (BiGRU) cells in the context of speech emotion recognition (SER). We also propose a novel Attention ReLU GRU (AR-GRU) that employs attention-based Rectified Linear Unit (AReLU) activation within GRU and BiGRU cells. We demonstrate the effectiveness of AR-GRU on one exemplary application using the recently proposed network for SER namely Interaction-Aware Attention Network (IAAN). Our proposed method utilising AR-GRU within this network yields significant performance gain and achieves an unweighted accuracy of 68.3% (2% over the baseline) and weighted accuracy of 66.9 % (2.2 % absolute over the baseline) in four class emotion recognition on the IEMOCAP database.