@inproceedings{wang-etal-2025-qualispeech,
    title = "{Q}uali{S}peech: A Speech Quality Assessment Dataset with Natural Language Reasoning and Descriptions",
    author = "Wang, Siyin  and
      Yu, Wenyi  and
      Chen, Xianzhao  and
      Tian, Xiaohai  and
      Zhang, Jun  and
      Lu, Lu  and
      Tsao, Yu  and
      Yamagishi, Junichi  and
      Wang, Yuxuan  and
      Zhang, Chao",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.1150/",
    pages = "23588--23609",
    ISBN = "979-8-89176-251-0",
    abstract = "This paper explores a novel perspective to speech quality assessment by leveraging natural language descriptions, offering richer, more nuanced insights than traditional numerical scoring methods. Natural language feedback provides instructive recommendations and detailed evaluations, yet existing datasets lack the comprehensive annotations needed for this approach. To bridge this gap, we introduce QualiSpeech, a comprehensive low-level speech quality assessment dataset encompassing 11 key aspects and detailed natural language comments that include reasoning and contextual insights. Additionally, we propose the QualiSpeech Benchmark to evaluate the low-level speech understanding capabilities of auditory large language models (LLMs). Experimental results demonstrate that finetuned auditory LLMs can reliably generate detailed descriptions of noise and distortion, effectively identifying their types and temporal characteristics. The results further highlight the potential for incorporating reasoning to enhance the accuracy and reliability of quality assessments. The dataset can be found at https://huggingface.co/datasets/tsinghua-ee/QualiSpeech."
}