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        <title>Using Songs to Improve Kazakh Automatic Speech Recognition</title>
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            <namePart type="given">Núria</namePart>
            <namePart type="family">Bel</namePart>
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            <namePart type="given">Henk</namePart>
            <namePart type="family">van den Heuvel</namePart>
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            <namePart type="given">Nancy</namePart>
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    <abstract>Developing automatic speech recognition (ASR) systems for low-resource languages is hindered by the scarcity of transcribed corpora. This proof-of-concept study explores songs as an unconventional yet promising data source for Kazakh ASR. We curate a dataset of 3,013 audio-text pairs (about 4.5 hours) from 195 songs by 36 artists, segmented at the lyric-line level. Using Whisper as the base recogniser, we fine-tune models under seven training scenarios involving Songs, Common Voice Corpus (CVC), and FLEURS, and evaluate them on three benchmarks: CVC, FLEURS, and Kazakh Speech Corpus 2 (KSC2). Results show that song-based fine-tuning improves performance over zero-shot baselines. For instance, Whisper Large-V3 Turbo trained on a mixture of Songs, CVC, and FLEURS achieves 27.6% normalised WER on CVC and 11.8% on FLEURS, while halving the error on KSC2 (39.3% vs. 81.2%) relative to the zero-shot model. Although these gains remain below those of models trained on the 1,100-hour KSC2 corpus, they demonstrate that even modest song-speech mixtures can yield meaningful adaptation improvements in low-resource ASR. The dataset is released on Hugging Face for research purposes under a gated, non-commercial licence.</abstract>
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%0 Conference Proceedings
%T Using Songs to Improve Kazakh Automatic Speech Recognition
%A Yeshpanov, Rustem
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F yeshpanov-2026-using
%X Developing automatic speech recognition (ASR) systems for low-resource languages is hindered by the scarcity of transcribed corpora. This proof-of-concept study explores songs as an unconventional yet promising data source for Kazakh ASR. We curate a dataset of 3,013 audio-text pairs (about 4.5 hours) from 195 songs by 36 artists, segmented at the lyric-line level. Using Whisper as the base recogniser, we fine-tune models under seven training scenarios involving Songs, Common Voice Corpus (CVC), and FLEURS, and evaluate them on three benchmarks: CVC, FLEURS, and Kazakh Speech Corpus 2 (KSC2). Results show that song-based fine-tuning improves performance over zero-shot baselines. For instance, Whisper Large-V3 Turbo trained on a mixture of Songs, CVC, and FLEURS achieves 27.6% normalised WER on CVC and 11.8% on FLEURS, while halving the error on KSC2 (39.3% vs. 81.2%) relative to the zero-shot model. Although these gains remain below those of models trained on the 1,100-hour KSC2 corpus, they demonstrate that even modest song-speech mixtures can yield meaningful adaptation improvements in low-resource ASR. The dataset is released on Hugging Face for research purposes under a gated, non-commercial licence.
%R 10.63317/5hqonmz5roum
%U https://aclanthology.org/2026.lrec-1.431/
%U https://doi.org/10.63317/5hqonmz5roum
%P 5528-5537

Markdown (Informal)

[Using Songs to Improve Kazakh Automatic Speech Recognition](https://aclanthology.org/2026.lrec-1.431/) (Yeshpanov, LREC 2026)

• Using Songs to Improve Kazakh Automatic Speech Recognition (Yeshpanov, LREC 2026)

ACL

• Rustem Yeshpanov. 2026. Using Songs to Improve Kazakh Automatic Speech Recognition. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 5528–5537, Palma de Mallorca, Spain. ELRA Language Resource Association.