Emoception: An Inception Inspired Efficient Speech Emotion Recognition Network

Abstract

This research proposes a Deep Neural Network architecture for Speech Emotion Recognition called Emoception, which takes inspiration from Inception modules. The network takes speech features like Mel-Frequency Spectral Coefficients (MFSC) or Mel-Frequency Cepstral Coefficients (MFCC) as input and recognizes the relevant emotion in the speech. We use USC-IEMOCAP dataset for training but the limited amount of training data and large depth of the network makes the network prone to overfitting, reducing validation accuracy. The Emoception network overcomes this problem by extending in width without increase in computational cost. We also employ a powerful regularization technique, Multi-Task Learning (MTL) to make the network robust. The model using MFSC input with MTL increases the accuracy by 1.6% vis-à-vis Emoception without MTL. We report an overall accuracy improvement of around 4.6% compared to the existing state-of-art methods for four emotion classes on IEMOCAP dataset.