# Revise, Reason, and Recognize: LLM-Based Emotion Recognition via Emotion-Specific Prompts and ASR Error Correction

arXiv: https://arxiv.org/abs/2409.15551 · HTML: https://mlsanity.com/paper/2409.15551/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in Audio-language model reasoning, one of 17 directions in LLM reasoning.
- Builds on 14 of its 22 references that are in the map.
- First posted on arXiv 23 Sep 2024.

## Placed in

- LLM reasoning / [Audio-language model reasoning](https://mlsanity.com/lr/audio-language-model-reasoning/)

## Builds on (most cited first, 14 of 14)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formatting](https://mlsanity.com/paper/2310.11324/) (2023)
- [Large Language Models Understand and Can be Enhanced by Emotional Stimuli](https://mlsanity.com/paper/2307.11760/) (2023)
- [Generative Speech Recognition Error Correction with Large Language Models and Task-Activating Prompting](https://mlsanity.com/paper/2309.15649/) (2023)
- [Emotional Intelligence of Large Language Models](https://mlsanity.com/paper/2307.09042/) (2023)
- [NegativePrompt: Leveraging Psychology for Large Language Models Enhancement via Negative Emotional Stimuli](https://mlsanity.com/paper/2405.02814/) (2024)
- [Foundation Model Assisted Automatic Speech Emotion Recognition: Transcribing, Annotating, and Augmenting](https://mlsanity.com/paper/2309.08108/) (2023)
- [Refashioning Emotion Recognition Modelling: The Advent of Generalised Large Models](https://mlsanity.com/paper/2308.11578/) (2023)
- [Speech Emotion Recognition with ASR Transcripts: A Comprehensive Study on Word Error Rate and Fusion Techniques](https://mlsanity.com/paper/2406.08353/) (2024)
- [Large Language Model Based Generative Error Correction: A Challenge and Baselines for Speech Recognition, Speaker Tagging, and Emotion Recognition](https://mlsanity.com/paper/2409.09785/) (2024)
- [ASR and Emotional Speech: A Word-Level Investigation of the Mutual Impact of Speech and Emotion Recognition](https://mlsanity.com/paper/2305.16065/) (2023)
- [On Prompt Sensitivity of ChatGPT in Affective Computing](https://mlsanity.com/paper/2403.14006/) (2024)
- [LanSER: Language-Model Supported Speech Emotion Recognition](https://mlsanity.com/paper/2309.03978/) (2023)
- [Language Models (Mostly) Do Not Consider Emotion Triggers When Predicting Emotion](https://mlsanity.com/paper/2311.09602/) (2023)

## Built on by (most cited first, 5 of 5)

- [Large Language Models for Subjective Language Understanding: A Survey](https://mlsanity.com/paper/2508.07959/) (2025)
- [EmoSLLM: Parameter-Efficient Adaptation of LLMs for Speech Emotion Recognition](https://mlsanity.com/paper/2508.14130/) (2025)
- [Semi-Supervised Cognitive State Classification from Speech with Multi-View Pseudo-Labeling](https://mlsanity.com/paper/2409.16937/) (2024)
- [PMF-CEC: Phoneme-augmented Multimodal Fusion for Context-aware ASR Error Correction with Error-specific Selective Decoding](https://mlsanity.com/paper/2506.11064/) (2025)
- [How to Retrieve Examples in In-context Learning to Improve Conversational Emotion Recognition using Large Language Models?](https://mlsanity.com/paper/2506.20199/) (2025)

## Abstract (arXiv)

Annotating and recognizing speech emotion using prompt engineering has recently emerged with the advancement of Large Language Models (LLMs), yet its efficacy and reliability remain questionable. In this paper, we conduct a systematic study on this topic, beginning with the proposal of novel prompts that incorporate emotion-specific knowledge from acoustics, linguistics, and psychology. Subsequently, we examine the effectiveness of LLM-based prompting on Automatic Speech Recognition (ASR) transcription, contrasting it with ground-truth transcription. Furthermore, we propose a Revise-Reason-Recognize prompting pipeline for robust LLM-based emotion recognition from spoken language with ASR errors. Additionally, experiments on context-aware learning, in-context learning, and instruction tuning are performed to examine the usefulness of LLM training schemes in this direction. Finally, we investigate the sensitivity of LLMs to minor prompt variations. Experimental results demonstrate the efficacy of the emotion-specific prompts, ASR error correction, and LLM training schemes for LLM-based emotion recognition. Our study aims to refine the use of LLMs in emotion recognition and related domains.