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Kalvin Chang

Kalvin Chang has 3 papers in our library, in 2024–2026, most of them on Language models & pretraining, Speech & spoken language, English; often with David R. Mortensen, Kwanghee Choi, Alane Suhr.

3 papers in our library · 2024–2026

OpenAlex · h-index 3 · 23 works · 20 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 20 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #10401

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 California, BerkeleyBerkeley · United States2026 · 8 paperscurrent🎓Berkeley CollegeNew York · United States2026 · 1 papercurrent🎓Carnegie Mellon UniversityPittsburgh · United States2023–2025 · 8 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 9The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 3 worksComputer Science · 3 worksPsychology · 2 worksArtificial Intelligence · 3 worksLanguage and Linguistics · 3 worksExperimental and Cognitive Psychology · 2 worksSignal Processing · 1 workMedicine · 1 workPhysiology · 1 work

🧭 Topics 3Research subjects and languages the work is about.English · 1 workSpeech & spoken language · 1 workLanguage models & pretraining · 1 work

 Works by year

2020s

20261 paper20251 paper20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Self-supervised Speech Representations Still Struggle with African American Vernacular English (2024 · 10 citations)
    The paper asks whether self-supervised speech models, which have helped low-resource languages, can narrow the long-known ASR accuracy gap between African American Vernacular English (AAVE) and Mainstream American English (MAE).
  2. Leveraging Allophony in Self-Supervised Speech Models for Atypical Pronunciation Assessment (2025 · 8 citations)
    Assessing atypical pronunciation, such as dysarthric or non-native speech, requires telling unusual realizations of a phoneme apart from normal variation.
  3. Linear Script Representations in Speech Foundation Models Enable Zero-Shot Transliteration (2026)
    Multilingual ASR systems such as Whisper learn from web data in which one language may be transcribed in several writing systems.

All works

4 works of theirs in our library, the most cited first; 3 with a page here, the others link their original until we summarise them.

  1. Transformed Protoform Reconstruction ↗Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) · no summary yetYoung Min Kim et al.2023 · 16 citations
  2. Self-supervised Speech Representations Still Struggle with African American Vernacular EnglishInterspeechKalvin Chang, Yi-Hui Chou, Jiatong Shi, et al.2024 · 10 citations
  3. Leveraging Allophony in Self-Supervised Speech Models for Atypical Pronunciation AssessmentConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Kwanghee Choi, Eunjung Yeo, Kalvin Chang, et al.2025 · 8 citations
  4. Linear Script Representations in Speech Foundation Models Enable Zero-Shot TransliterationFindings of the Association for Computational LinguisticsRyan Soh-Eun Shim, Kwanghee Choi, Kalvin Chang, et al.2026

 Co-authors here

11 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.

 Cited by

Papers of our library cite their works 20 times (of the 149,439 papers whose bibliographies we read); 7 citing papers with a page here →.

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