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KARAKALPAK SPEECH CORPUS: THE FIRST BENCHMARK DATASET FOR AUTOMATIC SPEECH RECOGNITION

Niyetbay UteulievDSc, Head of department, Nukus state technical university, Nukus, UzbekistanKabul KhudaybergenovPhD, Kimyo International University in Tashkent, Tashkent, UzbekistanJabbar KudaybergenovSenior lecturer, Nukus state technical university, Nukus, UzbekistanTangirbergen KudaybergenovTeaching assistant, Nukus state technical university, Nukus, Uzbekistan

Zenodo (CERN European Organization for Nuclear Research)repository2026en

ABI

Annotatsiya

While large-scale pre-trained models have significantly advanced multilingual Automatic Speech Recognition (ASR), many low-resource languages remain under-served due to the scarcity of high-quality annotated speech corpora. This paper introduces the Karakalpak Speech Corpus (KSC), the first publicly available benchmark dataset for Karakalpak, a Turkic language spoken by over two million people primarily in Karakalpakstan. The corpus encompasses 50 hours of predominantly read speech. The data was collected from 25 native speakers with a balanced gender distribution. To establish a performance benchmark, we fine-tuned the Wav2Vec 2.0 architecture, specifically evaluating the efficacy of transfer learning from multilingual pre-trained models.

Mavzular

Speech Recognition and Synthesis Speech and Audio Processing Face recognition and analysis

Identifikatorlar

DOI: 10.5281/zenodo.19079670

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