  This paper introduces a novel approach to emotion detection in speech using
Large Language Models (LLMs). We address the limitation of LLMs in processing
audio inputs by translating speech characteristics into natural language
descriptions. Our method integrates these descriptions into text prompts,
enabling LLMs to perform multimodal emotion analysis without architectural
modifications. We evaluate our approach on two datasets: IEMOCAP and MELD,
demonstrating significant improvements in emotion recognition accuracy,
particularly for high-quality audio data. Our experiments show that
incorporating speech descriptions yields a 2 percentage point increase in
weighted F1 score on IEMOCAP (from 70.111\% to 72.596\%). We also compare
various LLM architectures and explore the effectiveness of different feature
representations. Our findings highlight the potential of this approach in
enhancing emotion detection capabilities of LLMs and underscore the importance
of audio quality in speech-based emotion recognition tasks. We'll release the
source code on Github.
