# 'Beach' to 'Bitch': Inadvertent Unsafe Transcription of Kids' Content on YouTube

arXiv: https://arxiv.org/abs/2203.04837 · HTML: https://mlsanity.com/paper/2203.04837/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 46 references that are in the map.
- First posted on arXiv 17 Feb 2022.

## Builds on (most cited first, 17 of 17)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [Language Models as Knowledge Bases?](https://mlsanity.com/paper/1909.01066/) (2019)
- [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://mlsanity.com/paper/1909.08053/) (2019)
- [RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models](https://mlsanity.com/paper/2009.11462/) (2020)
- [Object Hallucination in Image Captioning](https://mlsanity.com/paper/1809.02156/) (2018)
- [XLM-E: Cross-lingual Language Model Pre-training via ELECTRA](https://mlsanity.com/paper/2106.16138/) (2021)
- [All You Need is "Love": Evading Hate-speech Detection](https://mlsanity.com/paper/1808.09115/) (2018)
- [Hallucination In Object Detection -- A Study In Visual Part Verification](https://mlsanity.com/paper/2106.02523/) (2021)
- [Disturbed YouTube for Kids: Characterizing and Detecting Inappropriate Videos Targeting Young Children](https://mlsanity.com/paper/1901.07046/) (2019)
- [Are Chess Discussions Racist? An Adversarial Hate Speech Data Set](https://mlsanity.com/paper/2011.10280/) (2020)
- [Towards Real-time Mispronunciation Detection in Kids' Speech](https://mlsanity.com/paper/2003.01765/) (2020)
- [Correcting Automated and Manual Speech Transcription Errors using Warped Language Models](https://mlsanity.com/paper/2103.14580/) (2021)
- [A Comparison of Online Automatic Speech Recognition Systems and the Nonverbal Responses to Unintelligible Speech](https://mlsanity.com/paper/1904.12403/) (2019)

## Built on by (most cited first, 3 of 3)

- [SafeText: A Benchmark for Exploring Physical Safety in Language Models](https://mlsanity.com/paper/2210.10045/) (2022)
- [SNIFR : Boosting Fine-Grained Child Harmful Content Detection Through Audio-Visual Alignment with Cascaded Cross-Transformer](https://mlsanity.com/paper/2506.03378/) (2025)
- [KidSpeak: A General Multi-purpose LLM for Kids' Speech Recognition and Screening](https://mlsanity.com/paper/2512.05994/) (2025)

## Abstract (arXiv)

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 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.