@inproceedings{wang-etal-2025-benchmarking-contextual,
    title = "Benchmarking Contextual and Paralinguistic Reasoning in Speech-{LLM}s: A Case Study with In-the-Wild Data",
    author = "Wang, Qiongqiong  and
      Sailor, Hardik Bhupendra  and
      Liu, Tianchi  and
      Zhang, Wenyu  and
      Huzaifah, Muhammad  and
      Lertcheva, Nattadaporn  and
      Sun, Shuo  and
      Chen, Nancy F.  and
      Wu, Jinyang  and
      Aw, AiTi",
    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/author-page-yu-wang-polytechnic/2025.findings-emnlp.760/",
    doi = "10.18653/v1/2025.findings-emnlp.760",
    pages = "14133--14148",
    ISBN = "979-8-89176-335-7",
    abstract = "Recent speech-LLMs have shown impressive performance in tasks like transcription and translation, yet they remain limited in understanding the paralinguistic aspects of speech crucial for social and emotional intelligence. We propose CP-Bench, a benchmark for evaluating speech-LLMs on contextual paralinguistic reasoning the integration of verbal content with non-verbal cues like emotion and prosody. The benchmark includes two curated question answering (QA) datasets requiring both linguistic and empathetic understanding. We evaluate state-of-the-art speech-LLMs from both open and closed-source models and perform a comprehensive analysis across different question types. The top two models were further analyzed under temperature tuning to understand its effect on this task. Our benchmark reveals a key gap in existing evaluations and offers insights into building more context-aware and emotionally intelligent speech-capable LLMs."
}