Join our mailing list to get updates on our events, news, and the latest from the world of African language resources.

Your email is safe with us. We promise not to spam!
Please, consider giving your feedback on using Lanfrica so that we can know how best to serve you. To get started, .

Building Text and Speech Datasets for Low Resourced Languages: A Case of Languages in East Africa

Africa has over 2000 languages; however, those languages are not well represented in the existing Natural Language Processing ecosystem. African languages lack essential digital resources to be engaged effectively in the advancing language technologies. This growing gap has attracted researchers to empower and build resources for African languages to transfer the various Natural Language Processing methods to African languages. This paper discusses the process we took to create, curate and annotate language text and speech datasets for low-resourced languages in East Africa. This paper focuses on five languages. Four of the languages: Luganda, Runyankore-Rukiga, Acholi, and Lumasaaba, are majorly spoken in Uganda, and Kiswahili which is a majorly spoken language across East Africa. We have run baseline: machine translation models on the English - Luganda dataset in the parallel text corpora and Automatic Speech Recognition (ASR) models on the Luganda speech dataset. We recorded a BiLingual Evaluation Understudy (BLEU) score of 37 for the English-Luganda model and a BLEU score of 36.8 for the Luganda-English model. For the ASR experiments, we obtained a Word Error Rate (WER) of 33%.