@inproceedings{li-etal-2025-emo-rl,
    title = "{EMO}-{RL}: Emotion-Rule-Based Reinforcement Learning Enhanced Audio-Language Model for Generalized Speech Emotion Recognition",
    author = "Li, Pengcheng  and
      Zhao, Botao  and
      Kang, Zuheng  and
      Peng, Junqing  and
      Qu, Xiaoyang  and
      He, Yayun  and
      Wang, Jianzong",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/name-variant-enfa-fane/2025.findings-emnlp.1018/",
    doi = "10.18653/v1/2025.findings-emnlp.1018",
    pages = "18744--18754",
    ISBN = "979-8-89176-335-7",
    abstract = "Although large audio-language models (LALMs) have demonstrated remarkable capabilities in audio perception, their performance in affective computing scenarios, particularly in emotion recognition, reasoning, and subtle sentiment differentiation, remains suboptimal. Recent advances in reinforcement learning (RL) have shown promise in improving LALMs' reasoning abilities. However, two critical challenges hinder the direct application of RL techniques to speech emotion recognition (SER) tasks: (1) convergence instability caused by ambiguous emotional boundaries and (2) limited reasoning ability when using relatively small models (e.g., 7B-parameter architectures). To address these challenges, we propose EMO-RL, a novel framework incorporating reinforcement learning with two key innovations: Emotion Similarity-Weighted Reward (ESWR) and Explicit Structured Reasoning (ESR). Built upon pretrained LALMs, our method employs group-relative policy optimization with emotion constraints. Comprehensive experiments demonstrate that our EMO-RL training strategies can significantly enhance the emotional reasoning capabilities of LALMs, achieving state-of-the-art performance on the MELD and IEMOCAP datasets, and cross-dataset experiments prove the strong superiority of generalization."
}