Human-Guided Reasoning with Large Language Models for Vietnamese Speech Emotion Recognition

Researchers have developed a human-machine collaborative framework to improve Vietnamese Speech Emotion Recognition by integrating large language model-based reasoning and acoustic feature analysis, addressing challenges like ambiguous emotional expressions and limited annotated data. This method uses confidence-based routing to delegate uncertain cases to LLMs for deeper analysis, achieving up to 86.59% accuracy on a dataset of 2,764 samples, highlighting the potential of combining human guidance with AI models in low-resource environments.

Read the full article at arXiv cs.CL (NLP)

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