GigaAM Multilingual: Foundation Model for Underrepresented Central Asian Languages

Jul 14, 2026

Researchers introduce GigaAM Multilingual, a Conformer encoder pre-trained on 2 million hours of audio with a HuBERT-style objective, specifically targeting underrepresented Central Asian languages such as Kazakh, Kyrgyz, and Uzbek. The model employs cluster-level data balancing and domain-aware sampling to address data imbalance and head-language dominance. In evaluations, GigaAM Multilingual outperforms Whisper Large v3 and Omnilingual-1B on these target languages, particularly for spontaneous speech. The foundation encoder and ASR model are publicly released.

Why it matters: This work demonstrates a practical approach to improving speech recognition for underrepresented languages, helping to close a critical technology gap.

Full story at: arXiv Audio and Speech Processing ↗