  Speech emotion recognition is a challenging task and an important step
towards more natural human-machine interaction. We show that pre-trained
language models can be fine-tuned for text emotion recognition, achieving an
accuracy of 69.5% on Task 4A of SemEval 2017, improving upon the previous state
of the art by over 3% absolute. We combine these language models with speech
emotion recognition, achieving results of 73.5% accuracy when using provided
transcriptions and speech data on a subset of four classes of the IEMOCAP
dataset. The use of noise-induced transcriptions and speech data results in an
accuracy of 71.4%. For our experiments, we created IEmoNet, a modular and
adaptable bimodal framework for speech emotion recognition based on pre-trained
language models. Lastly, we discuss the idea of using an emotional classifier
as a reward for reinforcement learning as a step towards more successful and
convenient human-machine interaction.
