Speech technology / responsible-AI researcher focused on queer inclusion and participatory speech dataset curation.
2
papers here
2
reviewed by Evolve
0
first-author
0
corresponding
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works · OpenAlex
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h-index · OpenAlex
Bibliometric figures come from OpenAlex (2026-10-08) and cover the author's entire public record, not just the papers listed here. Reproducibility signal comes from Evolve's own reviews.
Research profile
Output—
No publication-count or venue data is available yet, so output cannot be scored. This axis would measure total scholarly production over time — papers, venues, and authorship roles — and can be populated only as more cataloged works are linked.
Impact—
Citation counts, h-index, and influence metrics are not present in the available data, so impact is left unscored. This axis would measure how widely the two 2026 papers are cited and referenced across the speech-technology and responsible-AI communities.
Focus0/100
Focus measures topical concentration. The radar currently records 0, with the field distribution split evenly (0.5/0.5) between 'speech technology / responsible AI' and its fairness-accountability-transparency variant — labels that overlap heavily. Based on only two 2026 cataloged papers, both addressing queer inclusion in speech datasets, the work is in fact tightly themed; the zero score more likely reflects label fragmentation and a very small sample than genuinely scattered research.
Collaboration28/100
Collaboration scores 28 on the radar, drawn from 3 distinct coauthors across the cataloged work. Brooklyn Sheppard, Anaelia Ovalle, and Levent Sagun recur as frequent collaborators. On both 2026 arXiv papers Williams appears in a middle author position without corresponding-author role, suggesting contribution within a small, stable team rather than independent or large-consortium leadership.
Activity—
No temporal publication-frequency data is available in this snapshot, so activity cannot be scored. This axis would measure publishing cadence and recent engagement — output per year and recency of contributions — beyond the two 2026 arXiv papers already cataloged.
Reproducibility20/100
Reproducibility scores 20, the mean readiness rating across 2 assessed papers — both rated low. The audit paper is descriptive with thin statistics, and the participatory framework is a position paper without a pilot or empirical validation. This is consistent with early conceptual and audit-stage work; artifacts, code, or data releases that would raise readiness are not yet evident in the catalog.
Research summary
Adina Williams is an early-stage profile built from a small public bibliographic snapshot (ORCID 0000-0001-5281-3343), covering two arXiv papers from 2026 in speech technology and responsible AI. Both works center on queer and LGBTQIA+ representation in speech datasets. The first audits 8 speech datasets and finds queer speakers make up just 0–1.4% of those recorded, then distills practical frictions around gender annotation and participatory data collection into a four-way taxonomy of tensions. The second contributes a conceptual four-phase participatory framework for queer speech dataset curation, grounded in a case study; the review judges it timely and well-motivated but unvalidated, with no pilot or empirical evidence yet. The field distribution splits evenly (0.5/0.5) between 'speech technology / responsible AI' and its fairness-accountability-transparency variant. Collaboration is compact: 3 distinct coauthors, with Brooklyn Sheppard, Anaelia Ovalle, and Levent Sagun recurring, and Williams in a middle, non-corresponding author position on both papers. Radar scores reflect the small base — collaboration 28, reproducibility 20 (mean across 2 papers, both rated low readiness), focus 0 — while output, impact, and activity lack the underlying data to be scored. The record reads as a focused, emerging research direction rather than an established body of work.
Collaboration pattern
The visible collaboration footprint is compact: 3 distinct coauthors, with Brooklyn Sheppard, Anaelia Ovalle, and Levent Sagun recurring across the cataloged work. On both 2026 arXiv papers Williams sits in a middle author position and is not the corresponding author, consistent with contributing inside a small, stable team. The radar's collaboration score of 28 reflects this limited but consistent co-authorship base; no large multi-institution consortia appear in the current snapshot.
Research keywords
Highlights
Audit finds queer speakers in just 0–1.4% of 8 speech datasets
A four-way taxonomy of practical tensions in queer dataset inclusion
Proposes a four-phase participatory framework for queer speech data curation
Timeline
- 2026Publishes an audit of 8 speech datasets quantifying queer representation at 0–1.4% and introducing a four-way taxonomy of practical tensions.
- 2026Releases a conceptual four-phase participatory framework for queer speech dataset curation, grounded in a queer case study.
- 2026Both arXiv papers appear in speech technology / responsible AI, co-authored with Brooklyn Sheppard, Anaelia Ovalle, and Levent Sagun.
Papers in the Evolve catalog
Frequent co-authors
About this profile
This profile is AI-structured by Evolve.Pub from public bibliographic data (OpenAlex) and Evolve's own paper reviews. It is not written or endorsed by the researcher unless the profile is claimed. Radar dimensions are computed from real bibliometrics with fixed formulas — raw figures are shown alongside; missing dimensions mean insufficient data, never zero. Errors? Claim the profile to correct or hide it.
- Bibliometric dimensions are computed from a very small snapshot: only 2 cataloged papers underlie the reproducibility, focus, and collaboration scores.
- Output, impact, and activity axes are unscored because publication counts and citation metrics are absent from the input data.
- No institution or country is available; this profile is name-resolved from public bibliographic data linked to ORCID 0000-0001-5281-3343.
- Both papers are 2026 arXiv preprints rated low on reproducibility readiness; peer-review status and venue acceptance are unknown.