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
Ongoing advancements in AI architecture, specifically state space models like Mamba, are now being rigorously tested against established benchmarks in diverse linguistic contexts.
This research provides crucial insights into the performance of cutting-edge AI models for speech recognition in underrepresented languages, impacting accessibility and the global application of AI.
The evaluation of Mamba for ASR in South African languages highlights a potential improvement in multilingual AI capabilities, especially for resource-constrained languages.
- · South African AI/Tech sector
- · Mamba (AI architecture)
- · Multilingual AI developers
- · Users of South African languages
- · Legacy ASR models without ongoing innovation
- · Companies neglecting diverse language markets
Improved accuracy and efficiency of ASR systems for South African languages using Mamba's architecture.
Increased investment and development of AI applications tailored for African linguistic diversity and local needs.
Reduced digital language barriers fostering greater economic inclusion and cultural preservation in these regions.
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Read at arXiv cs.CL