WaxalNet: Benchmarking and Adapting Speech Recognition for Spontaneous Speech in 19 African Languages

Abstract

Multilingual speech foundation models claim broad language coverage, but how well they handle spontaneous African speech, and how best to adapt them, remains unclear. We benchmark zero-shot and fine-tuned speech recognition on the WAXAL corpus of spontaneous speech in 19 African languages, covering Whisper, MMS, Omnilingual ASR, and SeamlessM4T-v2. Fine-tuning lowers macro word error rate (WER) from 52.3 for the best zero-shot model covering all 19 languages (Omnilingual-7B) to 36.2 for a 300M-parameter MMS model and 31.5 for Omnilingual with an LLM decoder, although zero-shot models are already competitive on a few languages. Using Omnilingual's shared encoder, we compare CTC and LLM decoders with encoder pretraining held fixed. Between MMS and Whisper, the better model depends on the language family, but with a shared encoder the LLM decoder has lower WER on 18 of 19 languages, so the family pattern more likely comes from pretraining than from the decoder. The LLM decoder is, however, about five times larger and much slower on CPU. Pooling languages into one model, by family or across all 19, costs only 0.6 to 1.0 WER on average relative to per-language models, a modest price for serving all languages with one model. Fine-tuned models remain usable on read speech, and for held-out languages neither training on the other languages nor in-context examples replace in-language fine-tuning. A native-speaker audit across all 19 languages characterizes failure modes such as repetition loops. We release the code and a suite of over 100 fine-tuned models.