Augmenting Generative Adversarial Networks for Speech Emotion Recognition

Siddique Latif, Muhammad Asim, Rajib Rana, Sara Khalifa, Raja Jurdak, Björn W. Schuller

Generative adversarial networks (GANs) have shown potential in learning emotional attributes and generating new data samples. However, their performance is usually hindered by the unavailability of larger speech emotion recognition (SER) data. In this work, we propose a framework that utilises the mixup data augmentation scheme to augment the GAN in feature learning and generation. To show the effectiveness of the proposed framework, we present results for SER on (i) synthetic feature vectors, (ii) augmentation of the training data with synthetic features, (iii) encoded features in compressed representation. Our results show that the proposed framework can effectively learn compressed emotional representations as well as it can generate synthetic samples that help improve performance in within-corpus and cross-corpus evaluation.

 DOI: 10.21437/Interspeech.2020-3194

Cite as: Latif, S., Asim, M., Rana, R., Khalifa, S., Jurdak, R., Schuller, B.W. (2020) Augmenting Generative Adversarial Networks for Speech Emotion Recognition. Proc. Interspeech 2020, 521-525, DOI: 10.21437/Interspeech.2020-3194.

  author={Siddique Latif and Muhammad Asim and Rajib Rana and Sara Khalifa and Raja Jurdak and Björn W. Schuller},
  title={{Augmenting Generative Adversarial Networks for Speech Emotion Recognition}},
  booktitle={Proc. Interspeech 2020},