Association for Computational Linguistics. In Proceedings of the First ACL Workshop on Ethics in Natural Language Processing, pages 53–59, Valencia, Spain. Gender and Dialect Bias in YouTube’s Automatic Captions. Anthology ID: W17-1606 Volume: Proceedings of the First ACL Workshop on Ethics in Natural Language Processing Month: April Year: 2017 Address: Valencia, Spain Venue: EthNLP SIG: Publisher: Association for Computational Linguistics Note: Pages: 53–59 Language: URL: DOI: 10.18653/v1/W17-1606 Bibkey: tatman-2017-gender Cite (ACL): Rachael Tatman. 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. The results show robust differences in accuracy across both gender and dialect, with lower accuracy for 1) women and 2) speakers from Scotland.
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. Abstract This project evaluates the accuracy of YouTube’s automatically-generated captions across two genders and five dialect groups.
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