@inproceedings{pan-etal-2026-comprehensive,
    title = "Comprehensive Benchmarking of Long-Form Speech Generation in Diverse Scenarios",
    author = "Pan, Changhao  and
      Yang, Rui  and
      Wang, Han  and
      Zhou, Zhuan  and
      He, Xuming  and
      Guo, Wenxiang  and
      Jiang, Ziyue  and
      Li, Ruiqi  and
      Zhang, Yu  and
      Wen, Chenyuhao  and
      Lei, Ke  and
      Yin, Xiang  and
      Lu, Jingyu  and
      Zhu, Zhiyuan  and
      Zhao, Zhou",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.112/",
    pages = "2365--2400",
    ISBN = "979-8-89176-395-1",
    abstract = "Recent advances in speech generation have enabled high-fidelity synthesis, yet systematic evaluation of models under long-context conditions remains largely underexplored. A comprehensive evaluation benchmark for long-form speech is indispensable for two reasons: 1) existing test scenarios are often confined to limited domains, creating a significant gap with the diverse downstream applications; 2) existing metrics overlook critical long-text factors such as consistency and coherence, failing to generalize reliably. To this end, we propose LFSBench, a comprehensive benchmark that decomposes ``long-form speech quality'' into specific, disentangled dimensions. LFSBench has three key properties. 1) Rich speech scenarios: Focusing on long-form speech generation and multi-speaker dialog generation, LFSBench covers acoustics, semantics, and expressiveness challenges, and consists of 1,101 samples spanning 17 common speech scenarios; 2) Comprehensive evaluation dimensions: Along the acoustics, semantics, and expressiveness axes, LFSBench defines an automated evaluation protocol with seven metrics to provide a comprehensive, accurate, and standardized assessment; 3) Valuable Insights: Through extensive experiments, we reveal that current models still struggle in highly expressive scenarios and exhibit a notable gap in consistency and hierarchy compared to real recordings."
}