Deep Learning Models for Speech Emotion Recognition
V.M. Praseetha and Sangil Vadivel
DOI : 10.3844/jcssp.2018.1577.1587
Journal of Computer Science
Volume 14, Issue 11
Emotions play a vital role in the efficient and natural human computer interaction. Recognizing human emotions from their speech is truly a challenging task when accuracy, robustness and latency are considered. With the recent advancements in deep learning now it is possible to get better accuracy, robustness and low latency for solving complex functions. In our experiment we have developed two deep learning models for emotion recognition from speech. We compare the performance of a feed forward Deep Neural Network (DNN) with the recently developed Recurrent Neural Network (RNN) which is known as Gated Recurrent Unit (GRU) for speech emotion recognition. GRUs are currently not explored for classifying emotions from speech. The DNN model gives an accuracy of 89.96% and the GRU model gives an accuracy of 95.82%. Our experiments show that GRU model performs very well on emotion classification compared to the DNN model.
© 2018 V.M. Praseetha and Sangil Vadivel. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.