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Group Relative Policy Optimisation Improves Multilingual Speech Recognition in Low-Resource Languages — arXiv:2610.30663

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.30663
License (as archived): CC0-1.0
Archived: 2026-10-25T20:52:59.298Z
SHA-256 of the archived text: d9b2ed7e3758a2033c3e55dec34c0b7581202d0567e7c26482a662b04d9c3444
Stable URL of this page: https://metraipolis.com/documento/dc_e6aed39df2fa

How to cite

Group Relative Policy Optimisation Improves Multilingual Speech Recognition in Low-Resource Languages — arXiv:2610.30663 — arXiv (en), https://arxiv.org/abs/2610.30663 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_e6aed39df2fa (Metraipolis archive dc_e6aed39df2fa, sha256 d9b2ed7e3758a2033c3e55dec34c0b7581202d0567e7c26482a662b04d9c3444)

BibTeX

@misc{dce6aed39df2fa,
  title = {Group Relative Policy Optimisation Improves Multilingual Speech Recognition in Low-Resource Languages — arXiv:2610.30663},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.30663},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_e6aed39df2fa; sha256 d9b2ed7e3758a2033c3e55dec34c0b7581202d0567e7c26482a662b04d9c3444},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.30663. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Autoregressive encoder-decoder speech recognition models trained with teacher forcing can suffer from exposure bias, limiting their ability to recover from their own prediction errors at inference time. Recent work has shown that reinforcement learning with Group Relative Policy Optimisation (GRPO) improves speech recognition in major languages with LLM-based models. We extend this line of work to smaller, non-LLM encoder-decoder ASR models, using Whisper-large-v3 as a case study across 51 African languages. The benefits of this approach are most pronounced when training data is scarce or when input audio is noisy or degraded. Our resulting model, SunflowerASR, achieves the best published macro-averaged WER on the AfriVox-v2 (0.227) and SimbaBench (0.372) benchmarks, and is publicly available.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Artificial Intelligence (cs.AI) ; Computation and Language (cs.CL).
Pagina del lavoro: https://arxiv.org/abs/2610.30663 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-25T20:47:56.849Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).