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Meizhu Liu

Meizhu Liu has 2 papers in our library, in 2026, most of them on Speech & spoken language, Dialogue systems; often with Tao Sheng.

2 papers in our library · 2026

OpenAlex · ORCID · h-index 14 · 46 works · 846 citations OpenAlex, 2026-10-14

📄 2 works in our library · 3 more in our library without a page yet · ⭐ Paper Stars ranking: #11612

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

🎓University of FloridaGainesville · United States2009–2026 · 20 paperscurrent🏢YahooSunnyvale · United States2014–2020 · 8 papers🏢SiemensPrinceton · United States2012–2014 · 4 papers🏢Sorrento TherapeuticsSan Diego · United States2022–2023 · 2 papers🏢Siemens HealthcareMalvern · United States2011 · 2 papers

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 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksSignal Processing · 2 works

🧭 Topics 2Research subjects and languages the work is about.Speech & spoken language · 2 worksDialogue systems · 1 work

 Works by year

2020s

20262 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Synthetic Doctor-Patient Dialogue Generation for Robust Medical ASR: A Scalable Pipeline for Vocabulary Expansion and Privacy Preservation (2026 · 1 citation)
    General-purpose speech recognizers such as Whisper make many errors on clinical speech, particularly on drug names. Real annotated medical audio is scarce because of privacy rules and the cost of labeling. Liu and Liu address this with a fully synthetic data pipeline that needs no real patient recordings.
  2. No Label? No Problem: Unsupervised Continual Learning for Adaptive Medical ASR (2026)
    Medical speech recognition often fails on drug names, disease terms and other specialist vocabulary. The difficulty grows with accents, noisy clinical rooms and a steady stream of new terminology. Labeled clinical audio is expensive, needs expert annotators and is limited by privacy rules. The authors present MeSR…

All works

5 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

  1. Synthetic Doctor-Patient Dialogue Generation for Robust Medical ASR: A Scalable Pipeline for Vocabulary Expansion and Privacy PreservationConference of the European Chapter of the Association for Computational Linguistics (EACL)Kefei Liu, Meizhu Liu2026 · 1 citation
  2. No Label? No Problem: Unsupervised Continual Learning for Adaptive Medical ASRConference of the European Chapter of the Association for Computational Linguistics (EACL)Meizhu Liu, Tao Sheng2026
  3. Do Image–Text Metrics Respect Semantic Invariances? ↗Findings of the Association for Computational Linguistics: ACL 2026 · no summary yetAmit Agarwal et al.2026
  4. Think Twice Before You Write — an Entropy-based Decoding Strategy to Enhance LLM Reasoning ↗arXiv (Cornell University) · no summary yetJiashu He et al.2026
  5. Robust Audio-Text Retrieval via Cross-Modal Attention and Hybrid Loss ↗arXiv (Cornell University) · no summary yetMeizhu Liu et al.2026

 Co-authors here

1 co-author 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

This page in Markdown → https://md.research.elamur.ai/free/en/authors/meizhu-liu/