Data Augmentation Using GANs for Speech Emotion Recognition

In this work, we address the problem of data imbalance for the task of Speech Emotion Recognition (SER). We investigate conditioned data augmentation using Generative Adversarial Networks (GANs), in order to generate samples for underrepresented emotions. We adapt and improve a conditional GAN architecture to generate synthetic spectrograms for the minority class. For comparison purposes, we implement a series of signal-based data augmentation methods. The proposed GAN-based approach is evaluated on two datasets, namely IEMOCAP and FEEL-25k, a large multi-domain dataset. Results demonstrate a 10% relative performance improvement in IEMOCAP and 5% in FEEL- 25k, when augmenting the minority classes.