  Spoken language evolves constrained by the economy of speech, which depends
on factors such as the structure of the human mouth. This gives rise to local
phonetic correlations in spoken words. Here we demonstrate that these local
correlations facilitate the learning of spoken words by reducing their
information content. We do this by constructing a locally-connected
tensor-network model, inspired by similar variational models used for many-body
physics, which exploits these local phonetic correlations to facilitate the
learning of spoken words. The model is therefore a minimal model of phonetic
memory, where "learning to pronounce" and "learning a word" are one and the
same. A consequence of which is the learned ability to produce new words which
are phonetically reasonable for the target language; as well as providing a
hierarchy of the most likely errors that could be produced during the action of
speech. We test our model against Latin and Turkish words. (The code is
available on GitHub.)
