@inproceedings{huang-etal-2026-cspb,
    title = "{CSPB}: Conversational Speech Processing Benchmark for Self-supervised Speech Models",
    author = "Huang, Zili  and
      Maciejewski, Matthew  and
      Garcia Perera, Leibny Paola  and
      Watanabe, Shinji  and
      Khudanpur, Sanjeev",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-long.275/",
    pages = "5878--5893",
    ISBN = "979-8-89176-380-7",
    abstract = "Recent advances in self-supervised learning (SSL) have led to powerful speech representation models, yet their robustness in real-world conversational settings remains largely untested. Most existing benchmarks focus on clean, single-speaker, single-channel audio, failing to reflect the complexities of natural human interaction{---}where background noise, reverberation, and overlapping speech are the norm. To bridge these critical gaps, we present the Conversational Speech Processing Benchmark (CSPB), a new benchmark designed to assess the robustness of SSL speech models in realistic conversational scenarios. CSPB is constructed from four multi-party datasets{---}AMI, AliMeeting, MMCSG, and DiPCo{---}and supports both single-channel and multi-channel evaluation. By releasing CSPB as an open-source toolkit, we aim to establish a unified framework for evaluating and advancing robust, spatially-aware self-supervised speech models."
}