About

Professor Claire Bowern is a faculty member at Yale University with a focus on linguistics. Her research encompasses language documentation, linguistic fieldwork, and the study of language evolution and cultural evolution. She has advised numerous students and postdoctoral researchers, contributing to the academic community through mentorship and scholarly publications. Her work is recognized for its depth in understanding language morphosyntax and the documentation of diverse languages.

Research topics

• Computer Science
• Linguistics
• Philosophy
• Psychology
• Speech recognition
• Programming language
• Artificial Intelligence
• Sociology
• Biology
• Machine Learning
• Acoustics
• Geography
• Anthropology
• Cognitive psychology
• Physics

Selected publications

• D-PLACE aggregated dataset

  Zenodo (CERN European Organization for Nuclear Research) · 2026-01-13

  datasetOpen access

  Cite the source of the dataset as: Kathryn R. Kirby, Russell D. Gray, Simon J. Greenhill, Fiona M. Jordan, Stephanie Gomes-Ng, Hans-Jörg Bibiko, Damián E. Blasi, Carlos A. Botero, Claire Bowern, Carol R. Ember, Dan Leehr, Bobbi S. Low, Joe McCarter, William Divale, and Michael C. Gavin. (2016). D-PLACE: A Global Database of Cultural, Linguistic and Environmental Diversity. PLoS ONE, 11(7): e0158391. doi:10.1371/journal.pone.0158391.

  PublisherDOI

• Kinbank: A global database of kinship terminology

  Zenodo (CERN European Organization for Nuclear Research) · 2026-02-06

  datasetOpen access

  The data repository for the Kinbank dataset

  PublisherDOI

• Demographic shifts, inter-group contact and environmental conditions drive language extinction and diversification

  Proceedings of the Royal Society B Biological Sciences · 2026-01-28

  articleOpen access

  Humans collectively use thousands of languages. The number of languages in a region (i.e. 'richness') varies widely. Empirical research has identified social, environmental, geographic and demographic factors associated with language richness. However, our understanding of causal mechanisms and variation in their effects over space has been limited by prior analyses focusing on correlation and assuming stationarity. Here we use process-based, spatially explicit stochastic models to simulate the emergence, expansion, contraction, fragmentation and extinction of language ranges. We varied parameter settings in these computer-simulated experiments to evaluate the extent to which different processes reproduce observed patterns of language richness in North America. We find that the majority of spatial variation in language richness is explained by models in which environmental and social constraints determine population density, random shocks alter population sizes more frequently at higher population densities, and population shocks are more frequently negative than positive. Language diversification occurs when populations split after reaching size limits, and when ranges fragment due to population contractions following negative shocks or due to contact with other groups expanding following positive shocks. These findings support theories arguing that environmental and social conditions, constraints on group sizes, outcomes of contact and shifting demographics all shape language richness.

  PublisherDOI

• A Tutorial for Video in Spoken Language Documentation

  Edinburgh Research Explorer (University of Edinburgh) · 2026-01-01

  articleOpen accessSenior author

  Spoken language always goes along with meaningful visible behavior, such as gesture and eye gaze. But while language use is multimodal, published recommendations and formal training in spoken language documentation tend to focus almost exclusively on the audio part of the signal. Therefore, this tutorial provides a practical guide to using video as part of a spoken language documentation project. We motivate why these projects should consider recording video, and we then describe the equipment needs, recording setups, and post-processing workflow required for collecting transcribable video. We also discuss the unique ethical/privacy concerns raised by video recording and archiving. Overall, our goal is to centralize and formalize the recommendations about video that have long circulated in oral form, or as grey literature, in documentation circles. The scripts in the supplementary materials are maintained <a href="https://urldefense.com/v3/__https://github.com/amaliaskilton/auto-ffmpeg__;!!PvDODwlR4mBZyAb0!RZQFbNOEre7MeT8vM-g_GcQq3N0JfiFc6Hif7Cf7NWfqL4DHxVpW4oOMYr_SnmAC3rtW_b6iMNJr_rqou2k$">here</a>.

  PublisherOA PDF

• Comparing Phonological Feature Sets for Low-Resource ASR

  University of Massachusetts (UMass) Amherst · 2026-03-14

  articleOpen accessSenior author

  In this paper, we explore an alternative ASR framework in which phonological features are predicted as an explicit intermediate representation, rather than predicting phones directly. Because feature systems encode cross-linguistically meaningful structure, this intermediate representation can reduce sample complexity by constraining what must be learned from limited data, while also enabling rapid adaptation to new languages through changes to the phone-to-feature mapping rather than retraining the model. As a result, this approach is particularly well suited to low-resource settings. We retrained Phonet models on two different feature sets to see the extent to which specific theories of phonological features facilitate better phoneme recognition, using a low-resourced language (Yan-nhangu, Pama-Nyungan) as a testing ground for performance. We use a naïve greedy decoding strategy to isolate the effect of feature set choice, and find that IPA features lead to the best transcription accuracy, followed closely by a featureless baseline.

  PublisherDOI

• Phlorest phylogeny derived from Bouckaert et al. 2018 'The origin and expansion of Pama–Nyungan languages across Australia'

  Zenodo (CERN European Organization for Nuclear Research) · 2025-11-11

  datasetOpen access

  Cite the source of the dataset as: Bouckaert RR, Bowern C & Atkinson QD. 2018. The origin and expansion of Pama–Nyungan languages across Australia. Nature Ecology and Evolution. 2: 741–749

  PublisherDOI

• Phlorest phylogeny derived from Bowern & Atkinson 2012 'Computational phylogenetics and the internal structure of Pama-Nyungan'

  Zenodo (CERN European Organization for Nuclear Research) · 2025-11-11

  datasetOpen access1st authorCorresponding

  Cite the source of the dataset as: Bowern C & Atkinson QD. 2012. Computational phylogenetics and the internal structure of Pama-Nyungan. Language, 88(4), 817-845.

  PublisherDOI

• Australian archaeolinguistics

  Oxford University Press eBooks · 2025-07-22

  book-chapter1st authorCorresponding

  Abstract This chapter discusses the linguistic, genetic, and archaeological stories of the Indigenous peoples of the area now known as Australia (the southern portion of Sahul). When attempting to synthesize information from genetics, archaeology, and language for the deep past of Sahul, we are confronted with several seeming contradictions. On the one hand, the picture from genetics emphasizes continuity: rapid and early expansion (above 40,000 years ago), followed by fairly stable regionalism and some subsequent gene flow. The linguistic picture, however, appears to show a heavy disjunction, with one family, Pama-Nyungan, spreading and replacing most of the languages of almost 90% of the continent within the past 7,000 years. The material record shows a combination of stability, regionalism, and shift. This chapter explores some of these questions.

  PublisherDOI

• Linguistically Informed Tokenization Improves ASR for Underresourced Languages

  ArXiv.org · 2025-10-07

  preprintOpen accessSenior author

  Automatic speech recognition (ASR) is a crucial tool for linguists aiming to perform a variety of language documentation tasks. However, modern ASR systems use data-hungry transformer architectures, rendering them generally unusable for underresourced languages. We fine-tune a wav2vec2 ASR model on Yan-nhangu, a dormant Indigenous Australian language, comparing the effects of phonemic and orthographic tokenization strategies on performance. In parallel, we explore ASR's viability as a tool in a language documentation pipeline. We find that a linguistically informed phonemic tokenization system substantially improves WER and CER compared to a baseline orthographic tokenization scheme. Finally, we show that hand-correcting the output of an ASR model is much faster than hand-transcribing audio from scratch, demonstrating that ASR can work for underresourced languages.

  PublisherOA PDFDOI

• Diachrony and Diachronica

  Diachronica · 2025-05-23 · 2 citations

  article1st authorCorresponding
  PublisherDOI

Recent grants

• Dynamics of Hunter-Gatherer Language Change

  NSF · $718k · 2008–2014

• Language as a Window on Prehistory

  NSF · $382k · 2014–2020

• CAREER: Pama-Nyungan Reconstruction and the Prehistory of Australia

  NSF · $407k · 2008–2014

• The Language of Bardi (BCJ) Precontact Narratives

  NSF · $13k · 2008–2012

Frequent coauthors

• Russell D. Gray

  University of Auckland

  51 shared

• Simon J. Greenhill

  University of Auckland

  48 shared

• Michael C. Gavin

  Colorado State University

  46 shared

• Damián E. Blasí

  45 shared

• Kathryn R. Kirby

  42 shared

• Hannah J. Haynie

  Kent State University

  32 shared

• Fiona M. Jordan

  31 shared

• Jakob Lesage

  29 shared

Labs

• Language and Brain LabPI

Education

• Ph.D., Linguistics

  Harvard University

  2004