Automatic Speech Recognition for African Low-Resource Languages: Challenges and Future Directions

The study investigates the challenges and future directions for Automatic Speech Recognition (ASR) systems in African low-resource languages. The primary aim is to address data scarcity, linguistic complexity, limited computational resources, acoustic variability, and ethical concerns, which hinder the development of ASR technologies for these languages. The research emphasizes the need for practical and inclusive strategies to advance ASR systems in the African context.

The paper identifies several key challenges, including the scarcity of annotated speech data, the complexity of African languages with rich morphology and tonal variations, and limited computational infrastructure. Ethical concerns such as algorithmic bias and privacy issues further complicate the development of ASR systems. These challenges represent both technological and socio-linguistic barriers that require urgent attention.

To overcome these challenges, the study suggests innovative approaches such as community-driven data collection, self-supervised and multilingual learning, and lightweight model architectures. Techniques that prioritize privacy are also highlighted as essential for ethical ASR development. Pilot projects involving various African languages demonstrate the feasibility and impact of customized solutions, including morpheme-based modeling and domain-specific ASR applications in sectors like healthcare and education.

The paper underscores the importance of interdisciplinary collaboration and sustained investment to address the distinct linguistic and infrastructural challenges faced by the continent. Future research should focus on expanding and diversifying datasets, improving computational efficiency, and developing ethical and inclusive ASR systems. These efforts are crucial for safeguarding linguistic diversity, enhancing digital accessibility, and promoting socioeconomic participation for speakers of African languages.

The study concludes with a progressive roadmap for creating efficient and inclusive ASR systems that can adapt to the diverse linguistic landscape of Africa. By leveraging community engagement and advanced modeling techniques, researchers can develop ASR technologies that are both effective and equitable. The paper calls for continued collaboration among linguists, technologists, policymakers, and local communities to ensure that African languages are supported and preserved in the digital age.