From Monolingual to Multilingual: Evaluating Mamba for ASR in South African Languages

arXiv cs.CL4w4 min read

arXiv:2607.01502v1 Announce Type: new Abstract: Recent advances in automatic speech recognition (ASR) have explored different sequence models, including Conformer-based models and newer state space models such as Mamba. Although prior work has evaluated these architectures in multiple languages, their effectiveness in African languages remains underexplored. In this work, we evaluate Mamba for ASR on seven South African languages. In monolingual experiments, each model is trained on 50 hours of speech per language, and we compare Mamba to a Conformer baseline of similar parameter scale. Mamba

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From Monolingual to Multilingual: Evaluating Mamba for ASR in South African Languages

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