Researchers have evaluated the Mamba architecture for automatic speech recognition (ASR) in seven South African languages, comparing its performance to a Conformer baseline. Mamba demonstrated comparable accuracy to Conformer while requiring fewer computational resources and training faster. Multilingual training with Mamba improved performance over monolingual approaches, though explicit language information did not enhance in-domain accuracy but did boost cross-corpus robustness. Ablation studies in low-resource settings showed that language embeddings were beneficial, acting as task-specific control vectors rather than capturing linguistic similarity. AI
IMPACT Mamba shows potential for efficient and effective multilingual ASR, particularly in under-resourced language contexts.
RANK_REASON Academic paper detailing model evaluation on specific languages and tasks. [lever_c_demoted from research: ic=1 ai=1.0]
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