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        <title>No Label? No Problem: Unsupervised Continual Learning for Adaptive Medical ASR</title>
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            <namePart type="given">Yevgen</namePart>
            <namePart type="family">Matusevych</namePart>
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    <abstract>Automatic Speech Recognition (ASR) plays an important role in healthcare but faces unique challenges. Medical audio often contains specialized terminology, such as medication names, which existing ASR systems struggle to transcribe accurately. High error rates arise from pronunciation variability, the continual introduction of new terms, and the scarcity of high-quality labeled data—whose collection is costly and requires medical expertise. Although synthetic datasets partially alleviate this problem, they fail to capture the noise and variability of real-world recordings. Moreover, ASR models trained in controlled environments are highly sensitive to noise, leading to degraded performance in clinical settings. To address these limitations, we propose an unsupervised continual learning ASR framework that adapts to new data while preserving prior knowledge. This enables efficient domain adaptation without extensive retraining. Experiments on real-world medical audio demonstrate significant improvements over state-of-the-art baselines.</abstract>
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%0 Conference Proceedings
%T No Label? No Problem: Unsupervised Continual Learning for Adaptive Medical ASR
%A Liu, Meizhu
%A Sheng, Tao
%Y Matusevych, Yevgen
%Y Eryiğit, Gülşen
%Y Aletras, Nikolaos
%S Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 5: Industry Track)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-384-5
%F liu-sheng-2026-label
%X Automatic Speech Recognition (ASR) plays an important role in healthcare but faces unique challenges. Medical audio often contains specialized terminology, such as medication names, which existing ASR systems struggle to transcribe accurately. High error rates arise from pronunciation variability, the continual introduction of new terms, and the scarcity of high-quality labeled data—whose collection is costly and requires medical expertise. Although synthetic datasets partially alleviate this problem, they fail to capture the noise and variability of real-world recordings. Moreover, ASR models trained in controlled environments are highly sensitive to noise, leading to degraded performance in clinical settings. To address these limitations, we propose an unsupervised continual learning ASR framework that adapts to new data while preserving prior knowledge. This enables efficient domain adaptation without extensive retraining. Experiments on real-world medical audio demonstrate significant improvements over state-of-the-art baselines.
%U https://aclanthology.org/2026.eacl-industry.24/
%P 330-337

Markdown (Informal)

[No Label? No Problem: Unsupervised Continual Learning for Adaptive Medical ASR](https://aclanthology.org/2026.eacl-industry.24/) (Liu & Sheng, EACL 2026)

• No Label? No Problem: Unsupervised Continual Learning for Adaptive Medical ASR (Liu & Sheng, EACL 2026)

ACL

• Meizhu Liu and Tao Sheng. 2026. No Label? No Problem: Unsupervised Continual Learning for Adaptive Medical ASR. In Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 5: Industry Track), pages 330–337, Rabat, Morocco. Association for Computational Linguistics.