  Emotions lie on a continuum, but current models treat emotions as a finite
valued discrete variable. This representation does not capture the diversity in
the expression of emotion. To better represent emotions we propose the use of
natural language descriptions (or prompts). In this work, we address the
challenge of automatically generating these prompts and training a model to
better learn emotion representations from audio and prompt pairs. We use
acoustic properties that are correlated to emotion like pitch, intensity,
speech rate, and articulation rate to automatically generate prompts i.e.
'acoustic prompts'. We use a contrastive learning objective to map speech to
their respective acoustic prompts. We evaluate our model on Emotion Audio
Retrieval and Speech Emotion Recognition. Our results show that the acoustic
prompts significantly improve the model's performance in EAR, in various
Precision@K metrics. In SER, we observe a 3.8% relative accuracy improvement on
the Ravdess dataset.
