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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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
Citations
Cites
“Librispeech: an ASR corpus based on public domain audio books,”
Panayotov, V., Chen, G., Povey, D., & Khudanpur, S. (2015) · 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2015.7178964 · Cited by 6,072
Praat, a system for doing phonetics by computer
Boersma, P. (2002) · UvA-DARE (University of Amsterdam) · Cited by 4,417
TIMIT Acoustic-Phonetic Continuous Speech Corpus
Garofolo, J. S., Lamel, L. F., Fisher, W. M., Pallett, D. S., Dahlgren, N. L., Zue, V. W., & Fiscus, J. G. (2020) · Americanae (AECID Library) · doi:10.35111/17gk-bn40 · Cited by 2,509
Torralba, A., & Efros, A. A. (2011) · CVPR 2011 · doi:10.1109/cvpr.2011.5995347 · Cited by 2,440
SWITCHBOARD: telephone speech corpus for research and development
Godfrey, J. J., Holliman, E. C., & McDaniel, J. (1992) · [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing · doi:10.1109/icassp.1992.225858 · Cited by 2,134 · cited 1×
“Switchboard (Godfrey et al., 1992) undersamples women, Southerns and non-college-educated speakers.”
BackgroundSex, covert prestige and linguistic change in the urban British English of Norwich
TRUDGILL, P. W. (1972) · Language in Society · doi:10.1017/s0047404500000488 · Cited by 1,515
The whole woman: Sex and gender differences in variation
Eckert, P. (1989) · Language Variation and Change · doi:10.1017/s095439450000017x · Cited by 1,021
Rapid adaptation to foreign-accented English
Clarke, C. M., & Garrett, M. F. (2004) · The Journal of the Acoustical Society of America · doi:10.1121/1.1815131 · Cited by 626
The effect of experience on the perception and representation of dialect variants
Sumner, M., & Samuel, A. G. (2009) · Journal of Memory and Language · doi:10.1016/j.jml.2009.01.001 · Cited by 364
The social impact of natural language processing
Hovy, D., & Spruit, S. L. (2016) · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) · doi:10.18653/v1/p16-2096 · Cited by 329
The microsoft 2016 conversational speech recognition system
Xiong, W., Droppo, J., Huang, X., Seide, F., Seltzer, M., Stolcke, A., Yu, D., & Zweig, G. (2017) · 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2017.7953159 · Cited by 326
Real English: The Grammar Of English Dialects In The British Isles
Milroy, J., & Milroy, L. (2014) · Cited by 296
Demographic dialectal variation in social media: a case study of African-American English
Blodgett, S. L., Green, L., & O'Connor, B. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d16-1120 · Cited by 291
Johnson, E. (1998) · Language · doi:10.2307/417880 · Cited by 248
Gelfer, M. P., & Mikos, V. A. (2005) · Journal of Voice · doi:10.1016/j.jvoice.2004.10.006 · Cited by 190
The Dictionary of American Regional English
Shortridge, J. R. (1986) · Geographical Review · doi:10.2307/214786 · Cited by 189
Liao, H., McDermott, E., & Senior, A. (2013) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru.2013.6707758 · Cited by 188
Large Margin Hidden Markov Models for Automatic Speech Recognition
Sha, F., & Saul, L. K. (2007) · The MIT Press eBooks · doi:10.7551/mitpress/7503.003.0161 · Cited by 177
Goldwater, S., Jurafsky, D., & Manning, C. D. (2010) · Speech Communication · doi:10.1016/j.specom.2009.10.001 · Cited by 166
Gender recognition from speech. Part I: Coarse analysis
Wu, K., & Childers, D. G. (1991) · The Journal of the Acoustical Society of America · doi:10.1121/1.401663 · Cited by 156
Hay, J. B., Maclagan, M., & Gordon, E. (2008) · Edinburgh University Press eBooks · doi:10.1515/9780748630882 · Cited by 134
New telephone speech corpora at CSLU
Cole, R. A., Noel, M., Lander, T., & Durham, T. (1995) · European Conference on Speech Communication and Technology (Eurospeech) · doi:10.21437/eurospeech.1995-188 · Cited by 129
Pépiot, E. (2014) · Speech prosody · doi:10.21437/speechprosody.2014-49 · Cited by 101
THE POPULATION OF THE UNITED STATES
(1925) · Science · doi:10.1126/science.61.1582.xii-t · Cited by 95
Prosodic and other cues to speech recognition failures
Hirschberg, J., Litman, D. J., & Swerts, M. G. J. (2004) · Speech Communication · doi:10.1016/j.specom.2004.01.006 · Cited by 89
The Social Impact Of Natural Language Processing
Hovy, D., & Spruit, S. L. (2017) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.1000924 · Cited by 80
GDNN: a gender-dependent neural network for continuous speech recognition
Konig, Y., & Morgan, N. (2003) · 2022 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.1992.226966 · Cited by 33
Hiring by Algorithm: Predicting and Preventing Disparate ImpactPreprint
Ajunwa, I., Friedler, S. A., Scheidegger, C., & Venkatasubramanian, S. (2016) · SSRN Electronic Journal · Cited by 33
Wheatley, B., & Picone, J. (1991) · Digital Signal Processing · doi:10.1016/1051-2004(91)90095-3 · Cited by 27
Speaker-independent ASR for Modern Standard Arabic: effect of regional accents
Droua-Hamdani, G., Selouani, S., & Boudraa, M. (2012) · International Journal of Speech Technology · doi:10.1007/s10772-012-9146-4 · Cited by 18
The Population of the United States.
McNicoll, G., Anderton, D. L., Barrett, R., & Bogue, D. J. (1997) · Population and Development Review · doi:10.2307/2137588 · Cited by 15
Sawalha, M., & Shariah, M. A. (2013) · White Rose Research Online (University of Leeds, The University of Sheffield, University of York) · Cited by 10
Geo-location dependent deep neural network acoustic model for speech recognition
Ye, G., Liu, C., & Gong, Y. (2016) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2016.7472803 · Cited by 6
Assessing the efficacy of benchmarks for automatic speech accent recognition
Bock, B. R., & Shamir, L. (2015) · EAI International Conference on Mobile Multimedia Communications (MobiMedia) · doi:10.4108/icst.mobimedia.2015.259033 · Cited by 6
The Population of the United States
P., P., & Bogue, D. J. (1962) · Population · doi:10.2307/1525771 · Cited by 4
ATAROS Technical Report 1: Corpus collection and initial task validation
Freeman, V., Chan, J., Levow, G., WRIGHT, R. A., Mari Ostendorf, E., Zayats, V., Luan, Y., Morrison, H., Fox, L., Antoniak, M., et al. (2014) · Cited by 3
Cited by
Sorted by citations
On the Opportunities and Risks of Foundation Models
Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., et al. (2021) · arXiv · doi:10.48550/arxiv.2108.07258 · Cited by 2,676
Baker, R. S., & Hawn, A. (2022) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-021-00285-9 · Cited by 993
Bender, E. M., & Friedman, B. (2018) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00041 · Cited by 854
Racial disparities in automated speech recognition
Koenecke, A., Nam, A., Lake, E., Nudell, J., Quartey, M., Mengesha, Z., Toups, C., Rickford, J. R., Jurafsky, D., & Goel, S. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1915768117 · Cited by 765
Measuring and Mitigating Unintended Bias in Text Classification
Dixon, L., Li, J., Sorensen, J., Thain, N., & Vasserman, L. (2018) · Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3278721.3278729 · Cited by 674
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
