Cracking Arbitrariness: A Data-driven Study of Auditory Iconicity

Auditory iconic words display a phonological profile that imitates their referents’ sounds. Traditionally, those words are thought to constitute a minor portion of the auditory lexicon. In this article, we challenge this assumption by assessing the pervasiveness of onomatopoeia through a novel data-driven procedure. We embed spoken words and natural sounds into a shared auditory space through (a) a short-time Fourier transform, (b) a convolutional neural network trained to classify sounds, and (c) a network trained on speech recognition. Then, we employ the obtained vector representations to measure their objective auditory resemblance. These similarity indexes show that imitation is not limited to some circumscribed semantic categories, but instead can be considered as a widespread mechanism underlying the structure of the English auditory vocabulary. We finally empirically validate our similarity indexes as measures of iconicity against human judgments.