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The main aim is to develop a decoder-only speech language model for Kazakh using limited data resources.

February 12, 2026Applied SciencesOpen Access

When Data Is Scarce: Training a Kazakh Speech Language Model from Discrete Units

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The main aim is to develop a decoder-only speech language model for Kazakh using limited data resources.
Leveraged discrete acoustic units from self-supervised speech representations.
Utilized a pretrained Wav2Vec 2.0 model for feature extraction.
Employed k-means clustering to create discrete semantic tokens.
Curated a specialized Kazakh speech corpus from multiple audio datasets.
Evaluated system efficacy using negative log-likelihood and perplexity metrics.
Model captures Kazakh vowel harmony but has difficulty with long-range agglutinative structures.
High sensitivity to the quality of data and tokenization techniques was observed.
Despite limited data and hardware constraints, the model identified structural patterns in Kazakh speech.

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This research develops a speech language model for Kazakh, highlighting the challenges and implications of scarce data.

Kairatuly et al. (2026) studied this question.

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