SBIR Phase I: Artificial Intelligence (AI)-Enabled African Language Database

The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project is creating a tonally proficient Artificial Intelligence (AI)-enabled translation database for African languages. There are no such product or service that can accurately translate African languages, as African languages have traditionally been under-resourced by Western corporations. By 2050, almost 25% of the earth’s population will be Sub-Saharan African and currently more than 60% of Africans are under 25 years old. The African continent is projected to have $5.6 trillion in consumer and business spending by 2024 and the U.S. is investing over $350 million to expand digital access and literacy and promote U.S. corporate investment in the continent. By expanding opportunities to accurately translate and learn African languages, this project will support economic growth for both the U.S. and African countries and support health and welfare by facilitating communication with African-speaking Americans and recent immigrants. African languages are very diverse with more than 2000 distinct languages across the continent. They are difficult for non-native speakers to learn and for translation apps to correctly interpret, primarily due to the tonal and guttural sounds and slight pronunciation differences that make similar sounding words have completely different meanings. The proposed AI-enabled database is first-of-its kind. The project will establish the data processing, model training, and database evaluation steps necessary to produce AI-enabled databases. The goal is to train a database to decipher these tonal shifts and ensure that the correct meaning is conveyed, beginning with a large dataset of correctly spoken audio and visual examples of words and phrases. The primary objective of this project is to develop the entry-level, consistent, Machine Learning (ML) core functionalities and multimodal interactions in a database that can be utilized in the creation of other tonally based language ML/AI databases. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.