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Acoustic domain shift in spoken language identification from systematic domain generalization evaluation to real-world application — arXiv:2609.31759

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.31759
License (as archived): CC0-1.0
Archived: 2026-10-07T22:39:26.807Z
SHA-256 of the archived text: 1c6f0944e022491144dca098cffb91122aed68dffb0f61463d2aa778f3615a8b
Stable URL of this page: https://metraipolis.com/documento/dc_2fcdfa62a985

How to cite

Acoustic domain shift in spoken language identification from systematic domain generalization evaluation to real-world application — arXiv:2609.31759 — arXiv (en), https://arxiv.org/abs/2609.31759 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_2fcdfa62a985 (Metraipolis archive dc_2fcdfa62a985, sha256 1c6f0944e022491144dca098cffb91122aed68dffb0f61463d2aa778f3615a8b)

BibTeX

@misc{dc2fcdfa62a985,
  title = {Acoustic domain shift in spoken language identification from systematic domain generalization evaluation to real-world application — arXiv:2609.31759},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.31759},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_2fcdfa62a985; sha256 1c6f0944e022491144dca098cffb91122aed68dffb0f61463d2aa778f3615a8b},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.31759. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Domain Generalization (DG) aims to develop models that remain robust to conditions unseen during training. While DG has been systematically studied in computer vision through controlled benchmarks and diverse distribution shifts, its evaluation in spoken language recognition remains less structured. Existing speech datasets provide valuable benchmarks for robustness to real-world acoustic conditions, but are primarily designed around specific scenarios and large scale rather than as general-purpose tools for systematically constructing and evaluating domain shifts. In this work, we introduce a smallscale speech dataset and evaluation protocol for controlled studies of acoustic domain shifts. It enables the evaluation of spoken language recognition models under a variety of acousitc domain shifts. We introduce the speech modality into the DomainBed Domain Generalization framework and evaluate three domain generalization algorithms. We show that in-domain performance is not a reliable predictor of cross-domain robustness and verify that explicit domain invariant algorithms such as MMD or DANN algorithms do not outperform ERM. We further validate the generality of these findings on MMS-LID-126, a state-of-the-art spoken language identification system. We release the code.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-29 in cs.LG).
Categorie arXiv: Audio and Speech Processing (eess.AS) ; Machine Learning (cs.LG); Sound (cs.SD).
Pagina del lavoro: https://arxiv.org/abs/2609.31759 — 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 2026-10-07T22:14:28.537Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-09-29?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).