We present an extension of our previous work on multilingual NLP for Togolese languages by introducing new datasets, improved models, and a community-driven evaluation benchmark for Text-To-Speech (TTS). We expand the Eyaa-Tom multilingual corpus with additional speech data of about 26.9k recordings (30.9 hours) across 10 local languages, and incorporated 64.6k clips (46.6 hours) of Mozilla Common Voice contributions for Adja, Nawdm, Mina, and Tem to strengthen Automatic Speech Recognition (ASR) and speech synthesis. We detail how community contributors – including collaboration with a national TV journalist – helped collect and validate the Kabyè and French text, with an ethical compensation model in place. We fine-tune state-of-the-art models: OpenAI Whisper and faster-whisper, and Meta’s NLLB-200 model for machine translation across 11 languages (achieving 19.4 BLEU score for French→Ewe and 26.1 BLEU score for Kabyè→French). We also introduce the Lom Bench, a community-based benchmark where native speakers rate TTS output, indicating promising preliminary results in Mina and Togolese lingua franca french although further data is needed. We provide a comparative analysis of our results with recent multilingual systems, including Simba, Meta’s Omnilingual ASR, and UBC Toucan. Our work emphasizes practical pathways and how FAIR data sourcing and community participation can drive sustainable NLP development for underserved languages.

Most of the 40+ languages spoken in Togo are severely under-represented in Natural Language Processing (NLP) resources. We present YodiV3, a comprehensive approach to developing NLP for ten Togolese languages (plus two major lingua francas) covering machine translation, speech recognition, text-to-speech, and language identification. We introduce Eyaa-Tom, a new multi-domain parallel corpus (religious, healthcare, financial, etc.) for these languages. We also propose the Lom metric, a scoring framework to quantify the AI-readiness of each language in terms of available resources. Our experiments demonstrate that leveraging large pretrained models (e.g.NLLB for translation, MMS for speech) with YodiV3 leads to significant improvements in low-resource translation and speech tasks. This work highlights the impact of integrating diverse data sources and pretrained models to bootstrap NLP for under-served languages, and outlines future steps for expanding coverage and capability.