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It is well known that many machine learning systems demonstrate bias towards specific groups of individuals.
“Some statistical issues in the comparison of speech recognition algorithms,”
L. Gillick and S.J. Cox, · 1989
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“Tools for the analysis of benchmark speech recognition tests,”
D.S. Pallet, W.M. Fisher, and J.G. Fiscus, · 1990
Earlier work this paper cites.
An introduction to the bootstrap
Bradley Efron and Robert J Tibshirani, · 1994
Earlier work this paper cites.
“Fairness through awareness,”
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel, · 2012
Earlier work this paper cites.
“Sequence transduction with recurrent neural networks,”
Alex Graves, · 2012
Earlier work this paper cites.
“LibriSpeech: an ASR corpus based on public domain audio books,”
Vassil Panayotov, Guoguo Chen, Daniel Povey, and Sanjeev Khudanpur, · 2015
Earlier work this paper cites.
“Adam: A method for stochastic optimization,”
Diederik P Kingma and Jimmy Ba, · 2015
Earlier work this paper cites.
“Fair prediction with disparate impact: A study of bias in recidivism prediction instruments,”
Alexandra Chouldechova, · 2017
Earlier work this paper cites.
“Fairer and more accurate, but for whom?,”
Alexandra Chouldechova and Max G’Sell, · 2017
Earlier work this paper cites.
“Gender and dialect bias in YouTube’s automatic captions,”
Rachael Tatman, · 2017
Earlier work this paper cites.
Matt J Kusner, Joshua R Loftus, Chris Russell, and Ricardo Silva, · 2017
Earlier work this paper cites.
“Gender shades: Intersectional accuracy disparities in commercial gender classification,”
Joy Buolamwini and Timnit Gebru, · 2018
Earlier work this paper cites.
Taku Kudo and John Richardson, · 2018
Cited alongside, same era.
“Gender representation in French Broadcast Corpora and its impact on ASR performance,”
Mahault Garnerin, Solange Rossato, and Laurent Besacier, · 2019
Cited alongside, same era.
“SpecAugment: A simple data augmentation method for automatic speech recognition,”
Daniel S Park, William Chan, Yu Zhang, Chung-Cheng Chiu, Barret Zoph, Ekin D Cubuk, and Quoc V Le, · 2019
Cited alongside, same era.
“fairseq: A fast, extensible toolkit for sequence modeling,”
Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, and Michael Auli, · 2019
Cited alongside, same era.
“Racial disparities in automated speech recognition,”
Allison Koenecke, Andrew Nam, Emily Lake, Joe Nudell, Minnie Quartey, Zion Mengesha, Connor Toups, John R Rickford, Dan Jurafsky, and Sharad Goel, · 2020
Cited alongside, same era.
Accessed: 2021-09-10
“Understanding gender and racial bias in AI,” https://www.uxmatters.com/mt/archives/2020/11/understanding-gender-and-racial-bias-in-ai.php · 2021
Closest in time.
Accessed: 2021-09-10
“Racist algorithms,” https://medium.com/carre4/racist-algorithms-the-unspoken-bias-in-technology-fa45f309d7c8 · 2021
Closest in time.
“Casual conversations: A dataset for measuring fairness in AI,”
Caner Hazirbas, Joanna Bitton, Brian Dolhansky, Jacqueline Pan, Albert Gordo, and Cristian Canton Ferrer, · 2021
Closest in time.
“Quantifying bias in automatic speech recognition,”
Siyuan Feng, Olya Kudina, Bence Mark Halpern, and Odette Scharenborg, · 2021
Closest in time.
“Investigating the impact of gender representation in asr training data: a case study on librispeech,”
Mahault Garnerin, Solange Rossato, and Laurent Besacier, · 2021
Closest in time.
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“The corpus of regional African American Language,”
Tyler Kendall and Charlie Farrington, · 2020
Cited alongside, same era.
“Artie bias corpus: An open dataset for detecting demographic bias in speech applications,”
Josh Meyer, Lindy Rauchenstein, Joshua D Eisenberg, and Nicholas Howell, · 2020
Cited alongside, same era.
“Conformer: Convolution-augmented transformer for speech recognition,”
Anmol Gulati, James Qin, Chung-Cheng Chiu, Niki Parmar, Yu Zhang, Jiahui Yu, Wei Han, Shibo Wang, Zhengdong Zhang, Yonghui Wu, et al., · 2020
Cited alongside, same era.
Accessed: 2021-09-10
“Voice recognition still has significant race and gender biases,” https://hbr.org/2019/05/voice-recognition-still-has-significant-race-and-gender-biases · 2021
Cited alongside, same era.
Accessed: 2021-09-10
“Bridging the gender gap in AI,” https://www.forbes.com/sites/falonfatemi/2020/02/17/bridging-the-gender-gap-in-ai/?sh=73c6659e5ee8 · 2021
Cited alongside, same era.
Accessed: 2021-09-10
“AI voice recognition racially biased against black voices,” https://www.businessinsider.com/study-ai-voice-recognition-racially-biased-against-black-voices-2020-3 · 2021
Cited alongside, same era.
Accessed: 2021-09-10
“How to overcome cultural bias in voice AI design,” https://voices.soundhound.com/how-to-overcome-cultural-bias-in-voice-ai-design · 2021
Cited alongside, same era.
Leda Sari, Mark Hasegawa-Jonson, and Chang-D Yoo, · 2021
Closest in time.
“Streaming attention-based models with augmented memory for end-to-end speech recognition,”
Ching-Feng Yeh, Yongqiang Wang, Yangyang Shi, Chunyang Wu, Frank Zhang, Julian Chan, and Michael L Seltzer, · 2021
Closest in time.
“A better and faster end-to-end model for streaming ASR,”
Bo Li, Anmol Gulati, Jiahui Yu, Tara N Sainath, Chung-Cheng Chiu, Arun Narayanan, Shuo-Yiin Chang, Ruoming Pang, Yanzhang He, James Qin, et al., · 2021
Closest in time.
“Emformer: Efficient memory transformer based acoustic model for low latency streaming speech recognition,”
Yangyang Shi, Yongqiang Wang, Chunyang Wu, Ching-Feng Yeh, Julian Chan, Frank Zhang, Duc Le, and Mike Seltzer, · 2021
Closest in time.
“Alignment restricted streaming recurrent neural network transducer,”
Jay Mahadeokar, Yuan Shangguan, Duc Le, Gil Keren, Hang Su, Thong Le, Ching-Feng Yeh, Christian Fuegen, and Michael L Seltzer, · 2021
Closest in time.
“Improving rnn transducer based asr with auxiliary tasks,”
Chunxi Liu, Frank Zhang, Duc Le, Suyoun Kim, Yatharth Saraf, and Geoffrey Zweig, · 2021
Closest in time.