Data augmentation is used for speech emotion recognition tasks where certain emotional labels, e.g., sadness, are significantly underrepresented in a training dataset. This is typical for data collected in real-life applications. We propose conditioned data augmentation using Generative Adversarial Networks (GANs), in order to generate samples for underrepresented emotions. We propose 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. Results on the speech emotion recognition task show that the proposed data augmentation method significantly improves classification performance as compared to traditional speech data augmentation methods.
SPEECH DATA AUGMENTATION