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Spoken language evolves constrained by the economy of speech, which dependson factors such as the structure of the human mouth. This gives rise to localphonetic correlations in spoken words. Here we demonstrate that these localcorrelations facilitate the learning of spoken words by reducing theirinformation content. We do this by constructing a locally-connectedtensor-network model, inspired by similar variational models used for many-bodyphysics, which exploits these local phonetic correlations to facilitate thelearning of spoken words. The model is therefore a minimal model of phoneticmemory, where "learning to pronounce" and "learning a word" are one and thesame. A consequence of which is the learned ability to produce new words whichare phonetically reasonable for the target language; as well as providing ahierarchy of the most likely errors that could be produced during the action ofspeech. We test our model against Latin and Turkish words. (The code isavailable on GitHub.)

Minimal Effective Theory for Phonotactic Memory: Capturing Local Correlations due to Errors in Speech