The expansion of the speech technology sector has given rise to a novel economic model in language research, with the objective of developing speech datasets. This model is expanding to under-served African languages through collaborative efforts between industries, organisations, and the active participation of communities. This collaboration is yielding new datasets for machine learning, while also disclosing vulnerabilities and sociolinguistic discrepancies between industrialised and non-industrialised societies. A case study of a speech data collection camp that took place in September 2024 in Cameroon, involving representatives of 31 languages throughout the continent, illustrates both the prospects of the new economic model for research on under-served languages and the challenges of fair, effective, and responsible participation.

Orthographical standardization is a milestone in a language’s documentation and the development of its resources. However, texts written in former orthographies remain relevant to the language’s history and development and therefore must be converted to the standardized orthography. Ensuring a language has access to the orthographically standardized version of all of its recorded texts is important in the development of resources as it provides additional textual resources for training, supports contribution of authors using former writing systems, and provides information about the development of the language. This paper evaluates the performance of natural language processing methods, specifically Finite State Transducers and Long Short-term Memory networks, for the orthographical conversion of Bàsàá texts from the Protestant missionary orthography to the now-standard AGLC orthography, with the conclusion that LSTMs are somewhat more effective in the absence of explicit lexical information.