Emotion Label Encoding using Word Embeddings for Speech Emotion Recognition

Speech Emotion Recognition (SER) is an important and challenging task for human-computer interaction. Human emotions are complex and nuanced, hence difficult to represent. The standard representations of emotions, categorical or continuous, tend to oversimplify the problem. Recently, the label encoding approach has been proposed, where vectors are used to represent the emotion space. In this paper, we hypothesise that using a pre-existing vector space that encodes semantic information about emotion is beneficial for the task. To this aim, we propose using word embeddings obtained from a Language Model (LM) as labels for SER. We evaluate the performance of the proposed approach on the IEMOCAP corpus and show that it yields better performance than a standard baseline. We also present a method to combine free text labels, which are unusable in conventional approaches, and by doing so we show that the model can learn more nuanced representations of emotions.