Metraipolis › archive › dc_7377300a2785

Evaluating Tibetan Speech Recognition Across Dialects and Adaptation Regimes: A Multi-Granularity Analysis — arXiv:2610.27002

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.27002
License (as archived): CC0-1.0
Archived: 2026-10-21T07:08:47.101Z
SHA-256 of the archived text: 11450a9dcb1c07cf0844eda950eabc440334f5d9fdb0dc0f229f927af886fd9b
Stable URL of this page: https://metraipolis.com/documento/dc_7377300a2785

How to cite

Evaluating Tibetan Speech Recognition Across Dialects and Adaptation Regimes: A Multi-Granularity Analysis — arXiv:2610.27002 — arXiv (en), https://arxiv.org/abs/2610.27002 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_7377300a2785 (Metraipolis archive dc_7377300a2785, sha256 11450a9dcb1c07cf0844eda950eabc440334f5d9fdb0dc0f229f927af886fd9b)

BibTeX

@misc{dc7377300a2785,
  title = {Evaluating Tibetan Speech Recognition Across Dialects and Adaptation Regimes: A Multi-Granularity Analysis — arXiv:2610.27002},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.27002},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_7377300a2785; sha256 11450a9dcb1c07cf0844eda950eabc440334f5d9fdb0dc0f229f927af886fd9b},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.27002. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Tibetan speech recognition is usually evaluated with character or syllable error rates because the writing system does not mark word boundaries. These metrics can rank systems differently and respond differently to adaptation failures. We combine character error rate, syllable error rate, and segmented word error rate in one evaluation framework. We use dictionary-based, neural, and language-model-based segmentation and compare their boundaries with expert annotations. We evaluate five ASR architectures on Lhasa Tibetan, then test zero-shot transfer and target-corpus adaptation on five additional corpus subsets. We also vary LoRA rank and training-data size to examine adaptation failure. The results show that a lower character error does not always lead to a lower syllable or segmented-word error. These ranking differences persist across corpora. Metric correlations also weaken in some unsuccessful adaptation settings. Higher LoRA ranks improve syllable and segmented-word scores but remain worse than full fine-tuning in the tested range. A standardized phonological representation provides another view of spelling errors. These findings support evaluation at several linguistic levels and motivate better segmentation and adaptation methods for low-resource ASR.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.CL, nuovi lavori del giorno).
Categorie arXiv: Computation and Language (cs.CL).
Pagina del lavoro: https://arxiv.org/abs/2610.27002 — 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 2026-10-21T07:05:58.095Z dalla pagina https://arxiv.org/list/cs.CL/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).