GigaAM Multilingual: Foundation Model for Underrepresented Languages
Despite recent scaling successes, multilingual ASR performance remains highly uneven, with long-tail languages suffering from severe data scarcity. This work addresses the challenge of building robust foundation models for underrepresented Central Asian languages (Kazakh, Kyrgyz, Uzbek). We present GigaAM Multilingual, a Conformer encoder pre-trained on 2M hours of audio using a HuBERT-style objective. Crucially, we introduce a cluster-level data balancing strategy during pre-training and a domain-aware sampling method during fine-tuning to mitigate head-language dominance. In controlled comparisons, our approach outperforms strong open pretrained encoders (Whisper Large v3, Omnilingual-1...
| Authors | Andrei Kuzmenko, Alexandr Maximenko, Aleksandr Kutsakov et al. |
|---|---|
| Primary category | eess.AS |
| Published | 11 July 2026 |
| Last updated at source | 11 July 2026 |
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