The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
Gender and Dialect Bias in YouTube’s Automatic Captions
Open accessAbstract
This project evaluates the accuracy of YouTube's automatically-generated captions across two genders and five dialects of English.Speakers' dialect and gender was controlled for by using videos uploaded as part of the "accent tag challenge", where speakers explicitly identify their language background.The results show robust differences in accuracy across both gender and dialect, with lower accuracy for 1) women and 2) speakers from Scotland.This finding builds on earlier research finding that speaker's sociolinguistic identity may negatively impact their ability to use automatic speech recognition, and demonstrates the need for sociolinguistically-stratified validation of systems.
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“These models can perform well on average but poorly on some users (Tatman, 2017; Caldas et al., 2018; Li et al., 2019b; Koenecke et al., 2020).”
From WILDS: A Benchmark of in-the-Wild Distribution Shifts (2020) · cites this paper as Tatman, 2017
In the citing paper
“Performance disparities across individuals have been observed in a wide range of tasks and applications, including in natural language processing (Geva et al., 2019), automatic speech recognition (Koenecke et al., 2020; Tatman, 2017), federated learning (Li et al., 2019b; Caldas et al., 2018), and medical imaging (Badgeley et al., 2019).”
From WILDS: A Benchmark of in-the-Wild Distribution Shifts (2020) · cites this paper as Tatman, 2017
In the citing paper
“These models can perform well on average but poorly on some individuals (Tatman, 2017; Caldas et al., 2018; Li et al., 2019b; Koenecke et al., 2020).”
From WILDS: A Benchmark of in-the-Wild Distribution Shifts (2020) · cites this paper as Tatman, 2017
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Metadata
- Authors
- Tatman, R.
- Affiliations
- University of Washington (US)
- Corresponding author
- Tatman, Rachael
- Venue
- Proceedings of the First ACL Workshop on Ethics in Natural Language Processing
- Published
- 2017
- Pages
- 53-59
- OpenAlex
- W2607719644
- Cited by
- 338
- References
- 36
- Citations by year
- 2026: 34 · 2025: 33 · 2024: 35 · 2023: 50 · 2022: 56 · 2021: 62 · 2020: 33 · 2019: 21 · 2018: 11 · 2017: 3
- FWCI
- 8.48
- Type
- conference paper
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Funders
- National Science Foundation (1256082)
- University of Washington
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Social Sciences › Linguistics and Language › Linguistic Variation and Morphology
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Keywords
- speech recognition, speech recognition accuracy, gender bias, Scottish English, accent, sociolinguistic identity
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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