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This paper introduces GigaAM Multilingual, a Conformer encoder pre-trained on 2M hours of audio with a HuBERT-style objective, addressing data scarcity for Central Asian languages (Kazakh, Kyrgyz, Uzbek). It employs cluster-level data balancing and domain-aware sampling to outperform strong baselines like Whisper Large v3 on target languages.
This paper presents Flavors of Moonshine, a suite of tiny specialized ASR models for edge devices. The authors show that monolingual models trained on a balanced mix of human-labeled, pseudo-labeled, and synthetic data outperform larger multilingual models like Whisper, achieving state-of-the-art error rates for small models and enabling on-device ASR for underrepresented languages.