arXiv:2607. 04814v1 Announce Type: cross Abstract: Extending automatic speech recognition (ASR) to low-resource African languages is constrained by the prohibitive demands of data collection at scale.
By Andrei Florian, Cynthia Jayne Amol, Hope Kerubo Ombaba, Xiaoyu Cui, Boniface Mwau, Biatus Maina Kamau, Lilian Diana Awuor Wanzare, Christiane Fellbaum, Happy Buzaaba
arXiv:2606. 03304v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly evaluated in multilingual settings, yet their inference behavior in low-resource African languages remains underexplored especially under pure prompting without fine-tuning.
By Anuj Tiwari, Terry Oko-odion, Hannah Nwokocha
AfriSwitch is a 61.36‑hour, human‑transcribed benchmark of in‑the‑wild code‑switched speech covering 16 African languages and varieties, annotated with switch‑level English span tags, per‑utterance Code‑Mixing Index (CMI), and switch‑point counts. The corpus reveals that code‑switching behaviour varies widely across languages, with no single metric fully capturing how code‑switched a language is. Benchmarking five open and commercial multilingual ASR systems in a zero‑shot setting shows high word error rates, with the best system averaging 35.93% WER and none dropping below 24% on any language, indicating that Africa‑targeted training rather than model scale or nominal language coverage best predicts performance.
By Gabrial Zencha Ashungafac, Busayo Awobade, Tobi Olatunji
arXiv:2605. 02608v2 Announce Type: replace-cross Abstract: Transformer-based models achieve state-of-the-art dependency parsing for high-resource languages, yet their advantage over simpler architectures in low-resource settings remains poorly understood.
By Kevin Guan, Happy Buzaaba, Christiane Fellbaum
Large language models (LLMs) are increasingly evaluated in multilingual settings, yet their inference behavior in low-resource African languages remains underexplored especially under pure prompting without fine-tuning. We present a systematic study of prompting strategies for Natural Language Inference (NLI) in Swahili, Yoruba, and Hausa using the AfriXNLI benchmark.
TranslatePsy-AfriSLM is an open‑source machine‑translation resource set for 19 Sub‑Saharan African languages, comprising curated parallel data, African‑specialized synthetic data, and a family of fine‑tuned small language models (SLMs). The authors demonstrate that a unified quality‑estimation filtering can remove up to 96% of training tokens without harming quality, and that filtered synthetic data dominates the quality‑efficiency Pareto frontier. Models trained on this mixture outperform much larger systems such as TranslateGemma‑27B and Qwen3.5‑122B‑A10B, achieving superior performance with as few as 0.8 B parameters.
By Milan Gritta, Patrik Lambert, Jihye Back, Amril Nazir