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.

Beyond Laurel/Yanny: An Autoencoder-Enabled Search for Polyperceivable Audio

**Session Chair: ** Kyle Mahowald 
**Session Volunteer: ** Richard Plant

11B: Linguistic Theories, Cognitive Modeling and Psycholinguistics #1

technical paper

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Beyond Laurel/Yanny: An Autoencoder-Enabled Search for Polyperceivable Audio

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