Audio-Based Emotion Recognition Enhancement Through Progressive GANS

Training large-scale architectures such as Generative Adversarial Networks (GANs) in order to investigate audio-visual relations in emotion-enriched interactions is a challenging task. This procedure is hindered by the high complexity as well as the mode collapse phenomenon. Sufficiently training these architectures requires a massive amount of data. Furthermore, creating extensive audio-visual datasets for specific tasks, like emotion recognition, is a complicated task handicapped by the annotation cost and labelling ambiguities. On the other hand, it is much more forthright to get access to unlabeled audio-visual datasets mainly due to the easy access to online multimedia content. In this work, a progressive process for training GANs was conducted. The first step leverages enormous audio-visual unlabeled datasets to expose concealed cross-modal relationships. Meanwhile in the second step, a calibration of the weights by employing a limited amount of emotion annotated data was performed. Through experimentation, it was shown that our progressive GANs schema leads to a more efficient optimization of the whole network, and the generated samples from the target domain, when fused with the authentic ones, provides enhanced emotion recognition results.