learn.ifs.name

PUMA: language tokens for low-resource multilingual ASR

Oukid et al., 2026, PUMA (Findings of ACL 2026)

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4 cards in “2 Related work” · whole deck

2 Related work

  1. What weakness of fully shared multilingual end-to-end ASR models does the paper point out?
    With all parameters shared and no explicit language signal, they struggle to capture language-specific phonology and morphology.
    • Oukid et al., 2026, PUMA (Findings of ACL 2026), §2, p. 372
  2. What do self-supervised speech models (wav2vec 2.0, XLSR, XLS-R, USM) do well and badly, per PUMA's related work?
    They transfer well to new languages after fine-tuning, but they don't explicitly model language identity.
  3. How does MMS (Massively Multilingual Speech) handle many languages, and what is the cost?
    A shared 1B-parameter wav2vec 2.0 backbone plus a small adapter per language. Each adapter adds over 2M parameters, so the total grows linearly with the number of languages.
    • Oukid et al., 2026, PUMA (Findings of ACL 2026), §2–3.1, pp. 372–373
  4. Where does PUMA position itself relative to Whisper and Omnilingual ASR?
    Those reach strong (including zero-shot) coverage through very large capacity. PUMA aims instead at a compact, scalable model for low-resource settings.
    • Oukid et al., 2026, PUMA (Findings of ACL 2026), §2, p. 372

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