Kartik Chandra


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Beyond Laurel/Yanny: An Autoencoder-Enabled Search for Polyperceivable Audio
Kartik Chandra | Chuma Kabaghe | Gregory Valiant
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)

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 >2% of English words.