@inproceedings{kumar-etal-2026-rolling,
    title = "Rolling Out Data Quality Overnight, without losing the plot: A Multi-Agent System for Speech Data Quality Management",
    author = "Kumar, Rishabh  and
      Painuli, Abhinav  and
      Saji, Chriss Philip  and
      Soni, Devesh  and
      Krishna, Amrith  and
      Ramakrishnan, Ganesh",
    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.2062/",
    pages = "41449--41492",
    ISBN = "979-8-89176-395-1",
    abstract = "Quality management when creating large-scale speech datasets is essential for building reliable downstream models, yet verification pipelines are often brittle, domain-specific, and expertise-intensive. We introduce **SpeechQM-Agent**, a natural language-driven agentic framework that compiles user requirements into dependency-aware DAG workflows over modular tools for audio, transcript, and metadata verification. A central planner LLM enforces prerequisites and supports execution-time replanning (e.g., re-running failed steps or swapping tools), reducing manual pipeline engineering and improving robustness across heterogeneous vendor formats and multilingual settings. We also release **SpeechQM-Dataset**, a multilingual benchmark with controlled, vendor-inspired quality artifacts spanning 24 verification tasks. Across experiments, SpeechQM-Agent attains **80-90{\%}** agreement with expert verification while requiring **{\ensuremath{<}}20{\%}** of the cost and time of manual QC, and we further validate transfer to real vendor-supplied corpora. Planner LLM comparisons highlight fidelity-efficiency trade-offs."
}