Title: Variational Autoencoders for Learning Latent Representations of Speech Emotion: A Preliminary Study,
Abstract: Learning the latent representation of data in unsupervised fashion is a very
interesting process that provides relevant features for enhancing the
performance of a classifier. For speech emotion recognition tasks, generating
effective features is crucial. Currently, handcrafted features are mostly used
for speech emotion recognition, however, features learned automatically using
deep learning have shown strong success in many problems, especially in image
processing. In particular, deep generative models such as Variational
Autoencoders (VAEs) have gained enormous success for generating features for
natural images. Inspired by this, we propose VAEs for deriving the latent
representation of speech signals and use this representation to classify
emotions. To the best of our knowledge, we are the first to propose VAEs for
speech emotion classification. Evaluations on the IEMOCAP dataset demonstrate
that features learned by VAEs can produce state-of-the-art results for speech
emotion classification.