---
title: "Tone in Yoruba ASR — paper by Olusanya (2026)"
description: "Standard Yoruba is a low-resource language with three contrastive level tones (High, Mid, Low), written… 19 references, cited by 1 paper here."
authors: ["Joy Olusanya"]
year: 2026
venue: "Proceedings of the Second Workshop on Language Models for Low-Resource Languages (LoResLM 2026)"
source: "https://aclanthology.org/2026.loreslm-1.14/"
doi: "10.18653/v1/2026.loreslm-1.14"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/olusanya-2026-tone-yoruba-asr-evaluating-impact/"
---
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# Tone in Yoruba ASR: Evaluating the Impact of Tone Recognition on Transformer-Based ASR Models

**Authors:** Joy Olusanya · **Year:** 2026 · **Venue:** [Proceedings of the Second Workshop on Language Models for Low-Resource Languages (LoResLM 2026)](https://md.research.elamur.ai/free/en/venues/loreslm/) ([Workshop on Language Models for Low-Resource Languages 2026](https://md.research.elamur.ai/free/en/venues/loreslm/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.loreslm-1.14/) · [PDF](https://aclanthology.org/2026.loreslm-1.14.pdf) · [DOI 10.18653/v1/2026.loreslm-1.14](https://doi.org/10.18653/v1/2026.loreslm-1.14) · [OpenAlex](https://openalex.org/W7140093973)

**Citations:** 1 citations (OpenAlex, 2026-10-14) · 1 in our library, of the 149,439 papers whose bibliographies we read

## Summary
Standard Yoruba is a low-resource language with three contrastive level tones (High, Mid, Low), written with diacritics. Without these marks, one segmental string can stand for several unrelated words. The paper asks whether keeping tone marks in transcripts helps or hurts transformer-based speech recognition. Three pre-trained systems, Meta's MMS-1B-all, OpenAI's Whisper-small and AstralZander/Yoruba_ASR, were trained and tested on two versions of the data: fully tone-marked and with tone marks removed. Two metrics were used for evaluation: TER (Tone Error Rate) alongside WER (Word Error Rate). A small word-level subset with varying degrees of tonal specification was also examined qualitatively. In every case, the unmarked condition produced clearly lower errors. With diacritics, TER rose several-fold, and outputs showed flattened tones and homophone confusions. The author links this to tokenisation, weak encoding of pitch cues and tone-agnostic pre-training. The work shows that strong multilingual ASR models do not yet handle tone. It calls for tone-aware tokenisers, alignment, training objectives and metrics for tonal low-resource languages.

## Key points
- Models compared: MMS-1B-all, Whisper-small and AstralZander/Yoruba_ASR, each evaluated on tone-marked vs. non-tone-marked Yoruba data using WER and TER.
- TER without vs. with tone marks:
  - AstralZander: 6.44% → 30.94%
  - Whisper-small: 4.20% → 32.31%
  - MMS-1B-all: 5.20% → 52.37% (the largest increase)
- WER grew by more than 20% for every model once diacritics were included.
- In the word-level analysis, AstralZander preserved tone better than MMS-1B-all. MMS-1B-all often collapsed tonal contrasts, for example rendering *ògùn* ("medicine") as *ogun*, which can also mean "war" or the deity Ogun.
- Recommendations include specialised tokenizers for tonal languages, open and well-annotated Yoruba speech corpora, and metrics beyond WER such as TER and Semantic Error Rate (SER). Stated limitations are that no dialectal variation was covered and that the pre-trained models were not optimised for tone.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Joy Olusanya. 2026. Tone in Yoruba ASR: Evaluating the Impact of Tone Recognition on Transformer-Based ASR Models. Proceedings of the Second Workshop on Language Models for Low-Resource Languages (LoResLM 2026).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{olusanya2026tone,
  title = {{Tone in Yoruba ASR: Evaluating the Impact of Tone Recognition on Transformer-Based ASR Models}},
  author = {Joy Olusanya},
  booktitle = {Proceedings of the Second Workshop on Language Models for Low-Resource Languages (LoResLM 2026)},
  year = {2026},
  doi = {10.18653/v1/2026.loreslm-1.14},
  url = {https://doi.org/10.18653/v1/2026.loreslm-1.14}
}
```

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## References

19 references, 1 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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*Updated 31 October 2026*