论文标题
“ beach”到“ bit子”：YouTube上的不安全不安全的转录内容
'Beach' to 'Bitch': Inadvertent Unsafe Transcription of Kids' Content on YouTube
论文作者
论文摘要
在过去的几年中，YouTube的孩子们成为了儿童娱乐电视的竞争性替代品之一。因此，YouTube儿童的内容应获得额外的审查，以确保孩子的安全。尽管针对儿童的进攻性或不适当内容的研究正在获得动力，但很少有或根本没有研究AI应用程序在多大程度上（意外）引入不合适的儿童的内容。 在本文中，我们介绍了一部小说（令人不安的），发现众所周知的自动语音识别（ASR）系统可能会在转录YouTube儿童视频时产生非常不合适的文本内容。我们将这种现象称为\ emph {不适当的内容幻觉}。我们的分析表明，这种幻觉远非偶尔，ASR系统通常会充满信心地产生它们。我们发布了第一台音频的数据集，为儿童现有的最新ASR系统幻觉不适当的内容。 In addition, we demonstrate that some of these errors can be fixed using language models.
Over the last few years, YouTube Kids has emerged as one of the highly competitive alternatives to television for children's entertainment. Consequently, YouTube Kids' content should receive an additional level of scrutiny to ensure children's safety. While research on detecting offensive or inappropriate content for kids is gaining momentum, little or no current work exists that investigates to what extent AI applications can (accidentally) introduce content that is inappropriate for kids. In this paper, we present a novel (and troubling) finding that well-known automatic speech recognition (ASR) systems may produce text content highly inappropriate for kids while transcribing YouTube Kids' videos. We dub this phenomenon as \emph{inappropriate content hallucination}. Our analyses suggest that such hallucinations are far from occasional, and the ASR systems often produce them with high confidence. We release a first-of-its-kind data set of audios for which the existing state-of-the-art ASR systems hallucinate inappropriate content for kids. In addition, we demonstrate that some of these errors can be fixed using language models.
