This work addresses the challenge of building robust foundation models for underrepresented Central Asian languages by presenting GigaAM Multilingual, a Conformer encoder pre-trained on 2M hours of audio using a HuBERT-style objective, and introduces a cluster-level data balancing strategy during pre-training and a domain-aware sampling method during fine-tuning to mitigate head-language dominance.
Abstract
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-1B) on target languages, achieving significant gains on spontaneous speech while maintaining efficiency. We release the foundation encoder and ASR model, offering a proven recipe for effective multilingual adaptation under realistic data imbalance.
Unified Gradient Projection is proposed, which constrains parameter updates using reference gradients from language-balanced replay in a unified projection space and shows that combining gradient-level projection with data-level replay yields complementary gains in stability and plasticity.
Ziang Ren, Guodong Lin, Yuchen Ai et al.· 0 citations
Results demonstrate that MoLGE consistently outperforms dense multilingual baselines with a minimal increase in trainable parameters, and suggest that structured language specialization provides an effective pathway for massively scaling language coverage of multilingual ASR.
Sangmin Lee, Woo-Jin Chung, Woongjib Choi et al.· 0 citations
MEUSLI is introduced, the first open-science multilingual projector family that links a Whisper encoder with open-source multilingual LLMs, enabling fully open-source end-to-end ASR in 28 European languages.
Lorenzo Concina, Seraphina Fong, M. Matassoni et al.· 0 citations
Large Language Models (LLMs) have achieved remarkable performance largely due to the availability of high-quality and large-scale training data. However, this reliance creates a significant performance disparity between high-resource languages such as English and low-resource languages like Arabic. The imbalance in data exposure during the pre-training phase persists into the bilingual supervised fine-tuning (SFT) stage, limiting the models’ semantic depth and reasoning capabilities in low-resource languages.To address the aforementioned imbalance, we depart from traditional approaches by computing the optimal learning rates for each language in the dataset separately and transforming these learning rates into language-specific weighting coefficients used in the loss function. Experimental results obtained on the XQuAD test set after training on the MURI dataset demonstrate that the proposed method preserves performance in English while significantly improving Arabic performance, achieving 56.36 F1 and 40.27 Exact Match (EM) scores.
D. Özcan, Çağrı Yeşil, Hasan Saribas et al.· Signal Processing and Commun...· 0 citations
Despite the rapid advancement of Vision-Language Models (VLMs), their linguistic reach remains largely confined to high-resource languages, leaving the majority of the world's 7,000+ living languages on the wrong side of a growing digital divide. This disparity is especially pronounced in Optical Character Recognition (OCR), where low-resource scripts lack the massive datasets required for traditional scaling laws. We investigate OCR adaptation in extreme data-scarce regimes (<10K real and<250K synthetic images), demonstrating that conventional fine-tuning strategies often reach a performance ceiling. Our key finding reveals a structural inefficiency in language-specific adaptation: while higher layers of specialized models diverge to capture unique script nuances, the lower layers learn redundant, highly similar features. Motivated by this observation, we propose PSMC (Pre-train, Specialize, Merge, and Co-train), a data-efficient framework that capitalizes on a cross-script"transfer effect". Our approach first derives language-specific experts from a high-resource base model, then employs task arithmetic to fuse these experts into a unified, high-performance multilingual back- bone. Extensive evaluation across 10 Indian scripts (supporting 20+ languages) shows that PSMC achieves a ~2% average improvement in Word Recognition Rate (WRR) over individual specialist models without increasing parameter count. Our results indicate that joint training in the merged latent space facilitates a constructive knowledge transfer that benefits all constituent scripts, providing a scalable pathway for inclusive VLM development. Source code and datasets will be released post publication.
P. Achyuth, Kahaan Shah, Chetan Arora· 0 citations
MameLoshnLM, the first open-source 8B-parameter language model built specifically for Yiddish, is presented, providing both a foundation for Yiddish NLP and a practical template for language model development in historically rich but digitally underrepresented languages.
Uri Katz, Omer Goldman, Tomasz Limisiewicz et al.· 0 citations
