# Can Large Language Models Aid in Annotating Speech Emotional Data? Uncovering New Frontiers

arXiv: https://arxiv.org/abs/2307.06090 · HTML: https://mlsanity.com/paper/2307.06090/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jul 2023.

## Built on by (most cited first, 10 of 10)

- [LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methods](https://mlsanity.com/paper/2412.05579/) (2024)
- [AgenTracer: Who Is Inducing Failure in the LLM Agentic Systems?](https://mlsanity.com/paper/2509.03312/) (2025)
- [A Survey on Data Synthesis and Augmentation for Large Language Models](https://mlsanity.com/paper/2410.12896/) (2024)
- [A Survey on Data Augmentation in Large Model Era](https://mlsanity.com/paper/2401.15422/) (2024)
- [Data Augmentation using Large Language Models: Data Perspectives, Learning Paradigms and Challenges](https://mlsanity.com/paper/2403.02990/) (2024)
- [From Text to Emotion: Unveiling the Emotion Annotation Capabilities of LLMs](https://mlsanity.com/paper/2408.17026/) (2024)
- [Adversarial Text Purification: A Large Language Model Approach for Defense](https://mlsanity.com/paper/2402.06655/) (2024)
- [Rethinking Emotion Annotations in the Era of Large Language Models](https://mlsanity.com/paper/2412.07906/) (2024)
- [Scaling Ambiguity: Augmenting Human Annotation in Speech Emotion Recognition with Audio-Language Models](https://mlsanity.com/paper/2601.14620/) (2026)
- [Improving Speech-based Emotion Recognition with Contextual Utterance Analysis and LLMs](https://mlsanity.com/paper/2410.20334/) (2024)

## Abstract (arXiv)

Despite recent advancements in speech emotion recognition (SER) models, state-of-the-art deep learning (DL) approaches face the challenge of the limited availability of annotated data. Large language models (LLMs) have revolutionised our understanding of natural language, introducing emergent properties that broaden comprehension in language, speech, and vision. This paper examines the potential of LLMs to annotate abundant speech data, aiming to enhance the state-of-the-art in SER. We evaluate this capability across various settings using publicly available speech emotion classification datasets. Leveraging ChatGPT, we experimentally demonstrate the promising role of LLMs in speech emotion data annotation. Our evaluation encompasses single-shot and few-shots scenarios, revealing performance variability in SER. Notably, we achieve improved results through data augmentation, incorporating ChatGPT-annotated samples into existing datasets. Our work uncovers new frontiers in speech emotion classification, highlighting the increasing significance of LLMs in this field moving forward.