Learning Representations of Emotional Speech with Deep Convolutional Generative Adversarial Networks

Understand

Automatically assessing emotional valence in human speech has historically been a difficult task for machine learning algorithms.

• The subtle changes in the voice of the speaker that are indicative of positive or negative emotional states are often "overshadowed" by voice characteristics relating to emotional intensity or emotional activation.
• In this work we explore a representation learning approach that automatically derives discriminative representations of emotional speech.
• In particular, we investigate two machine learning strategies to improve classifier performance: (1) utilization of unlabeled data using a deep convolutional generative adversarial network (DCGAN), and (2) multitask learning.