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Gender and Dialect Bias in YouTube's Automatic Captions

机译:YouTube自动字幕中的性别和方言偏见

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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 so-ciolinguistic identity may negatively impact their ability to use automatic speech recognition, and demonstrates the need for sociolinguistically-stratified validation of systems.
机译:该项目评估了YouTube自动生成的字幕在两种性别和五种英语方言之间的准确性。演讲者的方言和性别是通过使用作为“口音标签挑战”的一部分上传的视频来控制的,演讲者在其中明确标识了其语言背景。结果表明,性别和方言的准确性存在明显差异,其中1)位女性和2)来自苏格兰的演讲者的准确性较低。此发现基于早期的研究发现,即说话者的社会语言认同可能会对他们使用自动语音识别产生负面影响,并证明了对系统进行社会语言分层验证的必要性。

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