{"paper":{"title":"WAXAL-NET: Finetuned Edge ASR Across 19 African Languages","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CY","cs.HC"],"primary_cat":"cs.CL","authors_text":"Akebert Arefaine, Athanase Bahizire, Bolarinwa Gbotemi, Candace Hunzwi, Cleophas Kadima, Emmanuel Aaron, Emmilly Namuganga, Hondi Prisca Birindwa, Idris Muhammed, Innocent Elendu Anyaele, John Uzodinma, Jonathan Enoch Simenya, Kausar Moshood, Martin Koome, Matewos Tegete Endaylalu, Mikel K. Ngueajio, Nicholaus Ladislaus, Oluwademilade Williams, Onitsiky Ranaivoson, Oreoluwa Babatunde, Pericles Adjovi, Peter Ifeoluwa Adeyemo, Prasenjit Mitra, Ramsey Njema, Sunday Ajayi, Toluwani Aremu, Ukachi Agnes Eze-Mbey, Victor Tolulope Olufemi, Wanchi Lucia Yen, Wongel Dawit Daniel, Yacoba Oduro-Yeboah","submitted_at":"2026-06-01T15:22:35Z","abstract_excerpt":"We evaluate whether compact domain-specialized ASR models can outperform massively multilingual foundation models for conversational African speech across 19 languages in the WAXAL corpus. Fine-tuned edge models achieve a macro-averaged WER of $38.0\\%$ compared to $64.9\\%$ for the best zero-shot baseline, a $26.9$ percentage-point reduction using models $3-40\\times$ smaller. Results confirm that domain specialization dominates scale for spontaneous African speech. Cross-domain evaluation shows that fine-tuned models recover usable performance on out-of-distribution (OOD) speech, while zero-sho"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.02375","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2606.02375/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}