🧑‍🔬 Kevin Tang

🏛️ Affiliations

🎓Heinrich Heine University DüsseldorfDüsseldorf · Germany2021–2026 · 54 papersEmployment since 2021 · University Professor in English Linguistics with specialisations in Phonetics/Phonology/Morphology, ​English Language and Linguisticscurrent🎓University of FloridaGainesville · United States2019–2026 · 33 papersEmployment 2019–2021 · Assistant Professor in Computational Language Science, LinguisticsEmployment since 2021 · Courtesy Assistant Professor in Computational Language Science (Linguistics), Linguisticscurrent🎓Liberal Arts UniversityYekaterinburg · Russia2025–2026 · 3 papers🎓University College LondonLondon · United Kingdom2010–2023 · 3 papersEducation 2010–2011 · M.A. in Linguistics, LinguisticsEducation 2011–2015 · Ph.D. in Linguistics, Linguistics🎓Zhejiang UniversityHangzhou · China2017–2019 · 3 papersEmployment 2017–2019 · Assistant Professor, Department of Linguistics🎓Yale UniversityNew Haven · United States2015–2017Employment 2015–2017 · ​ ​Postdoctoral Associate, Department of Linguistics🎓University of CambridgeCambridge · United Kingdom2006–2010Education 2006–2009 · B.A. in Engineering, EngineeringEducation 2009–2010 · M.Eng. in Engineering, Engineering

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 3 more institutions on single bylines are not listed.

🧭 Fields, topics and concepts

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

📅 Works by year

2020s

📄 Papers

The most cited first (OpenAlex and our library).

1. Automatic Speech Recognition Biases in Newcastle English: an Error Analysis (2025 · 25 citations)
   Commercial speech recognisers are known to be less accurate for some racial, gender and age groups, but regional dialect bias has received less attention.
2. HeiMorph at SIGMORPHON 2022 Shared Task on Morphological Acquisition Trajectories (2022 · 6 citations)
   The HeiMorph team took part in Part 2 of SIGMORPHON 2022 Task 0. The task asks a model to inflect words from very small training sets and checks whether its learning curve looks like child language acquisition. The data covered three patterns: English past tense, German noun plurals and Arabic noun plurals, with…
3. Disambiguation of morpho-syntactic features of African American English – the case of habitual be (2022 · 5 citations)
   The paper tackles a source of dialect bias in NLP. Habitual "be" in African American English (AAE), as in "I be in my office by 7:30", has the same surface form as ordinary uses of "be", and it is rare even in AAE data, so classifiers tend to miss it. The authors annotated 5,133 occurrences of "be" from the CORAAL…
4. Analysis of LLM as a grammatical feature tagger for African American English (2025 · 4 citations)
   The paper asks whether general-purpose LLMs can detect grammatical features of African American English (AAE), a low-resource variety, without any fine-tuning.
5. Automatic Speech Recognition of African American English: Lexical and Contextual Effects (2025 · 4 citations)
   The paper asks why end-to-end speech recognizers make more errors on African American English (AAE), focusing on two pronunciation features.
6. Leveraging Syntactic Dependencies in Disambiguation: The Case of African American English (2024 · 4 citations)
   African American English (AAE) has a "habitual be": an invariant, non-agreeing be that marks recurring situations, as in "I be in my office by 7:30." It is rare in the data and looks the same as ordinary be on the surface.
7. ParsTranslit: Truly Versatile Tajik-Farsi Transliteration (2026 · 1 citation)
   Persian is written in two scripts: Perso-Arabic in Iran and Afghanistan (called "Farsi" here) and Tajik-Cyrillic in Tajikistan.
8. A Sociolinguistic Analysis of Automatic Speech Recognition Bias in Newcastle English (2026)
   Commercial speech recognisers handle regional accents worse than mainstream ones. Earlier work had rarely identified which dialect features cause the failures or how error rates differ among speakers of the same community. This paper studies the problem using Newcastle (Tyneside) English. The authors ran spontaneous…
9. Character-aware Transformers Learn an Irregular Morphological Pattern Yet None Generalize Like Humans (2026)
   The paper asks whether character-level neural inflection models can act as cognitive models of how speakers learn morphological structure that has no functional basis.
10. Layer-wise Probing of wav2vec 2.0 and Whisper for Consonant Cluster Reduction in African American English (2026)
    Speech recognizers make more errors for speakers of African American English (AAE), and consonant cluster reduction (CCR) is one documented contributor.

🤝 Co-authors here

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

• Sarah Moeller ×3
• Akhilesh Kakolu Ramarao ×2
• Alice Rozet ×2
• Dana Serditova ×2
• Hamid Mojarad ×2
• Jotsna Gowda ×2

Venues

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

• Findings of the Association for Computational Linguistics ×2: Findings of the Association for Computational Linguistics 2026, Findings of the Association for Computational Linguistics 2025
• Interspeech ×2: Interspeech 2025 ×2
• Workshop on Cognitive Modeling and Computational Linguistics (CMCL): CMCL 2026
• Language Resources and Evaluation Conference (LREC): LREC 2024
• Workshop on Language Technology for Equality, Diversity, Inclusion: Workshop on Language Technology for Equality, Diversity, Inclusion 2022
• Workshop of the Special Interest Group on Computational Morphology and Phonology (SIGMORPHON): SIGMORPHON 2022

🔗 Cited by

Papers of our library cite their works 15 times (of the 149,439 papers whose bibliographies we read); 7 citing papers with a page here →.

Updated 31 October 2026

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