---
title: "PUMA — paper by Oukid et al. (2026) — summary and ideas"
description: "The authors propose PUMA, a compact multilingual ASR model that keeps all weights shared and signals the target language… 44 references."
authors: ["Ilyes Oukid", "Bilal Faye", "Hanane Azzag", "Mustapha Lebbah", "Said Yacine Boulahia"]
year: 2026
venue: "Findings of the Association for Computational Linguistics: ACL 2026"
source: "https://aclanthology.org/2026.findings-acl.17/"
doi: "10.18653/v1/2026.findings-acl.17"
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/oukid-2026-puma-projected-universal-multilingual-asr/"
---
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# PUMA: Projected Universal Multilingual ASR for Low-Resource Settings. Application to Diverse African Languages

**Authors:** Ilyes Oukid, Bilal Faye, Hanane Azzag, Mustapha Lebbah and Said Yacine Boulahia · **Year:** 2026 · **Venue:** [Findings of the Association for Computational Linguistics: ACL 2026](https://md.research.elamur.ai/free/en/venues/findings/) ([Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/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.findings-acl.17/) · [PDF](https://aclanthology.org/2026.findings-acl.17.pdf) · [DOI 10.18653/v1/2026.findings-acl.17](https://doi.org/10.18653/v1/2026.findings-acl.17) · [OpenAlex](https://openalex.org/W7166835148)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Multilingual speech recognition systems often perform poorly on under-resourced, linguistically varied languages. Adding per-language adapters can help, but each new language then adds parameters. The authors propose PUMA, a compact multilingual ASR model that keeps all weights shared and signals the target language through a single learnable language token. This token is prepended to frozen wav2vec 2.0 acoustic features and passed through a small trainable Universal Language Projection (ULP) module. The token is then removed, and the result is added back to the features before a frozen wav2vec 2.0 encoder and a CTC-trained output head.

The model was tested on ten African languages and Arabic under three regimes: per-language, progressive (sequential with replay), and joint multilingual training. PUMA beat strong multilingual baselines and stayed close to much larger systems in a cross-corpus zero-shot evaluation, with roughly 30M trainable parameters. This matters for frugal ASR, where a new language should cost only one extra token.

## Key points
- **Architecture.** The ULP module consists of 4 shared Transformer blocks with RMSNorm. The feature extractor, feature projection and encoder are frozen components from wav2vec 2.0, and training uses CTC loss.
- **Parameter budget.** Trainable parameters stay fixed at about 30M no matter how many languages are supported. By contrast, each MMS language adapter adds more than 2M parameters.
- **Zero-shot comparison.** PUMA outperformed Whisper (small, medium, large), MMS and Seamless-M4T v2 Large. Omnilingual ASR CTC-1B scored slightly better, and Omni-LLM-7B achieved the best results overall.
- **Ablation on the language token.** Removing the token raised word error rate in every language in Table 5 for which the extracted figures are legible. Examples: Fon rose from 0.207 to 0.296, Arabic from 0.384 to 0.505, and Yoruba from 0.245 to 0.297.
- **Stated limitation and future work.** The authors note that zero-shot ability is limited by the lightweight design. They plan to explore multi-token language representations and Mixture-of-Experts approaches.

*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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- [Centre National de la Recherche Scientifique](https://md.research.elamur.ai/free/en/labs/centre-national-de-la-recherche-scientifique/) — Research institute, Paris, France · 169 works · 62,488 citations
- [Laboratoire d'Informatique de Paris-Nord](https://md.research.elamur.ai/free/en/labs/laboratoire-d-informatique-de-paris-nord/) — Research institute, Villetaneuse, France · 10 works · 510 citations
- [Université Sorbonne Paris Nord](https://md.research.elamur.ai/free/en/universities/universite-sorbonne-paris-nord/) — University, Villetaneuse, France · 9 works · 468 citations
- [Université de Versailles Saint-Quentin-en-Yvelines](https://md.research.elamur.ai/free/en/universities/universite-de-versailles-saint-quentin-en-yvelines/) — University, Versailles, France · 2 works

Also on the byline: [Données et algorithmes pour une ville intelligente et durable](https://ror.org/01xta2p78).

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## Cite

Cite as: Ilyes Oukid, Bilal Faye, Hanane Azzag, Mustapha Lebbah and Said Yacine Boulahia. 2026. PUMA: Projected Universal Multilingual ASR for Low-Resource Settings. Application to Diverse African Languages. Findings of the Association for Computational Linguistics: ACL 2026.

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

```bibtex
@inproceedings{oukid2026puma,
  title = {{PUMA: Projected Universal Multilingual ASR for Low-Resource Settings. Application to Diverse African Languages}},
  author = {Ilyes Oukid, Bilal Faye, Hanane Azzag, Mustapha Lebbah and Said Yacine Boulahia},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.17},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.17}
}
```

- [← previous PUCP-Metrix: An Open-source and Comprehensive Toolkit for Linguistic Analysis of Spanish Texts](https://md.research.elamur.ai/free/en/papers/luis-2026-pucp-metrix-open-source-comprehensive/)
- [next → PUPPET: Neural-Symbolic Standardized Patients for Mental Health](https://md.research.elamur.ai/free/en/papers/xu-2026-puppet-neural-symbolic-standardized-patients/)
- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/46/)

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*Updated 1 November 2026*