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        <title>Building ASR Resources for the Hutsul Dialect of Ukrainian</title>
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    <abstract>Dialectal speech remains largely underexplored in Automatic Speech Recognition (ASR) research, particularly for Slavic languages. While Ukrainian ASR systems have rapidly improved in recent years with the adoption of Whisper, XLS-R, and Wav2Vec-based models, performance on dialectal variants remains unknown and often significantly degraded. In this work, we present the first dedicated effort to build ASR resources for the Hutsul dialect of Ukrainian. We develop a data preparation and segmentation pipeline, evaluate multiple forced alignment strategies, and benchmark state-of-the-art ASR models under zero-shot and fine-tuned conditions. We evaluate results using WER and CER demonstrating that large multilingual ASR models struggle with dialectal speech, while lightweight fine-tuning produces substantial improvements. All scripts, alignment tools, and training recipes are made publicly available to support future research on Ukrainian dialect speech.</abstract>
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%0 Conference Proceedings
%T Building ASR Resources for the Hutsul Dialect of Ukrainian
%A Kyslyi, Roman
%A Orlovskyi, Artem
%A Khomenko, Pavlo
%A Onyshchenko, Bohdan
%A Guzii, Zakhar
%S Proceedings of the 13th Workshop on NLP for Similar Languages, Varieties and Dialects
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%F kyslyi-etal-2026-building
%X Dialectal speech remains largely underexplored in Automatic Speech Recognition (ASR) research, particularly for Slavic languages. While Ukrainian ASR systems have rapidly improved in recent years with the adoption of Whisper, XLS-R, and Wav2Vec-based models, performance on dialectal variants remains unknown and often significantly degraded. In this work, we present the first dedicated effort to build ASR resources for the Hutsul dialect of Ukrainian. We develop a data preparation and segmentation pipeline, evaluate multiple forced alignment strategies, and benchmark state-of-the-art ASR models under zero-shot and fine-tuned conditions. We evaluate results using WER and CER demonstrating that large multilingual ASR models struggle with dialectal speech, while lightweight fine-tuning produces substantial improvements. All scripts, alignment tools, and training recipes are made publicly available to support future research on Ukrainian dialect speech.
%U https://aclanthology.org/2026.vardial-1.15/
%P 186-195

Markdown (Informal)

[Building ASR Resources for the Hutsul Dialect of Ukrainian](https://aclanthology.org/2026.vardial-1.15/) (Kyslyi et al., VarDial 2026)

• Building ASR Resources for the Hutsul Dialect of Ukrainian (Kyslyi et al., VarDial 2026)

ACL

• Roman Kyslyi, Artem Orlovskyi, Pavlo Khomenko, Bohdan Onyshchenko, and Zakhar Guzii. 2026. Building ASR Resources for the Hutsul Dialect of Ukrainian. In Proceedings of the 13th Workshop on NLP for Similar Languages, Varieties and Dialects, pages 186–195, Rabat, Morocco. Association for Computational Linguistics.