@inproceedings{orlovskyi-etal-2026-scaling,
    title = "Scaling {ASR} for Hutsul Dialect: Multi-Speaker Data Collection, Enhanced Transcription and Cross-Speaker Evaluation",
    author = "Orlovskyi, Artem  and
      Guzii, Zakhar  and
      Onyshchenko, Bohdan  and
      Kyslyi, Roman  and
      Khomenko, Pavlo",
    editor = "Romanyshyn, Mariana",
    booktitle = "Proceedings of the Fifth {U}krainian Natural Language Processing Conference ({UNLP} 2026)",
    month = may,
    year = "2026",
    address = "Lviv, Ukraine",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/corrections-2026-06/2026.unlp-1.16/",
    pages = "184--198",
    ISBN = "979-8-89176-359-3",
    abstract = "We present a significant expansion of ASR resources for the Hutsul dialect of Ukrainian, building on prior work that established the first aligned speech corpus from a single literary source. In this work, we scale the dataset from a single speaker to a multi-speaker corpus comprising 40 speakers and 60.63 hours of audio drawn from diverse sources: YouTube channels (with author permissions), field recordings from native speakers, linguist student recordings, and regional radio broadcasts. To obtain reference transcriptions for audio without existing text, we introduce a novel RAG-enhanced correction pipeline: audio is first transcribed using ElevenLabs, then corrected through a RAG pipeline backed by a dialect-aware language model. We evaluate a fine-tuned ASR models across five distinct speaker datasets, demonstrating that while the model achieves strong performance on in-domain speakers (CER 3.24{\%}), cross-speaker generalization remains challenging, with CER ranging from 5.33{\%} to 17.24{\%} depending on speaker characteristics. All data, code, and models are released publicly to support further research on Ukrainian dialect speech technologies."
}