@inproceedings{chandra-etal-2021-beyond,
    title = "Beyond Laurel/Yanny: An Autoencoder-Enabled Search for Polyperceivable Audio",
    author = "Chandra, Kartik  and
      Kabaghe, Chuma  and
      Valiant, Gregory",
    editor = "Zong, Chengqing  and
      Xia, Fei  and
      Li, Wenjie  and
      Navigli, Roberto",
    booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2021.acl-short.75/",
    doi = "10.18653/v1/2021.acl-short.75",
    pages = "593--598",
    abstract = "The famous ``laurel/yanny'' phenomenon references an audio clip that elicits dramatically different responses from different listeners. For the original clip, roughly half the population hears the word ``laurel,'' while the other half hears ``yanny.'' How common are such ``polyperceivable'' audio clips? In this paper we apply ML techniques to study the prevalence of polyperceivability in spoken language. We devise a metric that correlates with polyperceivability of audio clips, use it to efficiently find new ``laurel/yanny''-type examples, and validate these results with human experiments. Our results suggest that polyperceivable examples are surprisingly prevalent in natural language, existing for {\ensuremath{>}}2{\%} of English words."
}