This paper outlines the need for and ongoing development of automated speech recognition (ASR) models for people living with impaired speech in African languages and support innovation of apps and tools for functional use in everyday conversation. While English language ASR models exist for interpreting impaired speech, no known work has addressed language models for African languages. The Centre for Digital Language Inclusion (CDLI) was established to address this gap by creating technologies that support individuals with atypical speech in local languages and cultures, starting with ten African languages.

The development of ASR for impaired speech in Low Resource Languages (LRLs) faces significant barriers, primarily due to the lack of recorded speech samples. Existing datasets are almost exclusively in American English, with very limited representation of other languages, and even fewer LRLs. English-focused models often exhibit poor accuracy with how English is spoken in Africa. To overcome these challenges, CDLI adopts a community-led, user-centric research practice, involving partnering with local institutions to collect recordings of impaired speech, developing open-source tools for data collection and ASR model building, and providing technical training. CDLIs key principle is to democratise speech recognition technology by empowering local communities to create their own datasets and AI models. CDLI's work in Ghana with the Akan language serves as a pilot study towards this goal. The longer-term goal is to foster local, autonomous, and sustainable skills for creating inclusive ASR technologies that meet the specific needs of atypical speakers across Africa, and beyond.