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        <title>Towards large-scale speech foundation models for a low-resource minority language</title>
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        <namePart type="family">Hiovain-Asikainen</namePart>
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    <name type="personal">
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    <name type="personal">
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        <name type="personal">
            <namePart type="given">Richard</namePart>
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    <abstract>Modern ASR systems require massive amounts of training data. While ASR training data for most languages are scarce and expensive to transcribe, a practical solution is to collect huge amounts of raw untranscribed speech and pre-train the ASR model in a self-supervised manner. Unfortunately, for many low-resource minority languages, even untranscribed speech data are scarce. In this paper, we propose a solution for the Northern Sámi language with 22,400 hours of speech extracted from the Finnish radio and television archives. We evaluated the model performance with different decoding algorithms and examined the models’ internal behavior with interpretation-based techniques.</abstract>
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%0 Conference Proceedings
%T Towards large-scale speech foundation models for a low-resource minority language
%A Getman, Yaroslav
%A Grósz, Tamás
%A Hiovain-Asikainen, Katri
%A Lehtonen, Tommi
%A Kurimo, Mikko
%Y Johansson, Richard
%Y Stymne, Sara
%S Proceedings of the Joint 25th Nordic Conference on Computational Linguistics and 11th Baltic Conference on Human Language Technologies (NoDaLiDa/Baltic-HLT 2025)
%D 2025
%8 March
%I University of Tartu Library
%C Tallinn, Estonia
%@ 978-9908-53-109-0
%F getman-etal-2025-towards
%X Modern ASR systems require massive amounts of training data. While ASR training data for most languages are scarce and expensive to transcribe, a practical solution is to collect huge amounts of raw untranscribed speech and pre-train the ASR model in a self-supervised manner. Unfortunately, for many low-resource minority languages, even untranscribed speech data are scarce. In this paper, we propose a solution for the Northern Sámi language with 22,400 hours of speech extracted from the Finnish radio and television archives. We evaluated the model performance with different decoding algorithms and examined the models’ internal behavior with interpretation-based techniques.
%U https://aclanthology.org/2025.nodalida-1.19/
%P 192-200

Markdown (Informal)

[Towards large-scale speech foundation models for a low-resource minority language](https://aclanthology.org/2025.nodalida-1.19/) (Getman et al., NoDaLiDa 2025)

• Towards large-scale speech foundation models for a low-resource minority language (Getman et al., NoDaLiDa 2025)

ACL

• Yaroslav Getman, Tamás Grósz, Katri Hiovain-Asikainen, Tommi Lehtonen, and Mikko Kurimo. 2025. Towards large-scale speech foundation models for a low-resource minority language. In Proceedings of the Joint 25th Nordic Conference on Computational Linguistics and 11th Baltic Conference on Human Language Technologies (NoDaLiDa/Baltic-HLT 2025), pages 192–200, Tallinn, Estonia. University of Tartu Library.