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Ting Dang

Ting Dang has 2 papers in our library, in 2026, most of them on Low-resource languages, Speech & spoken language, Multilingual & cross-lingual NLP; often with Jing Yang, Jingdong Chen, Yang Xiao.

2 papers in our library · 2026

OpenAlex · h-index 1 · 42 works · 1 citations OpenAlex, 2026-10-14

📄 2 works in our library · ⭐ Paper Stars ranking: #13958

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🎓The University of MelbourneMelbourne · Australia2026 · 23 paperscurrent🎓Melbourne School of TheologyMelbourne · Australia2026 · 1 papercurrent

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 7The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 worksSignal Processing · 2 worksExperimental and Cognitive Psychology · 1 workPsychology · 1 work

🧭 Topics 4Research subjects and languages the work is about.Low-resource languages · 2 worksMultilingual & cross-lingual NLP · 1 workSemitic languages · 1 workSpeech & spoken language · 1 work

 Works by year

2020s

20262 papers

 Papers

The most cited first (OpenAlex and our library).

  1. A Fusion-Aware Two-Stage Framework for Mispronunciation Detection and Diagnosis in Low-Resource Modern Standard Arabic (2026)
    The paper tackles phoneme-level mispronunciation detection and diagnosis (MDD) for Modern Standard Arabic, where learner recordings are scarce and synthetic error data does not match real learner speech well.
  2. Adapting Where It Matters: Depth-Aware Adaptation for Efficient Multilingual Speech Recognition in Low-Resource Languages (2026)
    Speech foundation models handle high-resource languages well but degrade on low-resource ones. Full fine-tuning is costly and overfits on small datasets. LoRA-style methods spread the same adaptation budget over every layer. The authors first ran per-layer linear probes for language identification on Common Voice…

 Co-authors here

3 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

Updated

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