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

TL;DR

This paper analyzes the challenges faced in developing automatic speech recognition systems for African low-resource languages and proposes strategies to overcome data scarcity, linguistic complexity, and infrastructural limitations.

Contribution

It provides a comprehensive analysis of barriers and suggests inclusive, practical solutions like community data collection and multilingual learning for African ASR development.

Findings

Community-driven data collection improves language resources.

Self-supervised learning enhances model performance with limited data.

Customized solutions increase accessibility and impact.

Abstract

Automatic Speech Recognition (ASR) technologies have transformed human-computer interaction; however, low-resource languages in Africa remain significantly underrepresented in both research and practical applications. This study investigates the major challenges hindering the development of ASR systems for these languages, which include data scarcity, linguistic complexity, limited computational resources, acoustic variability, and ethical concerns surrounding bias and privacy. The primary goal is to critically analyze these barriers and identify practical, inclusive strategies to advance ASR technologies within the African context. Recent advances and case studies emphasize promising strategies such as community-driven data collection, self-supervised and multilingual learning, lightweight model architectures, and techniques that prioritize privacy. Evidence from pilot projects involving various African languages showcases the feasibility and impact of customized solutions, which encompass morpheme-based modeling and domain-specific ASR applications in sectors like healthcare and education. The findings highlight the importance of interdisciplinary collaboration and sustained investment to tackle the distinct linguistic and infrastructural challenges faced by the continent. This study offers a progressive roadmap for creating ethical, efficient, and inclusive ASR systems that not only safeguard linguistic diversity but also improve digital accessibility and promote socioeconomic participation for speakers of African languages.