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

TL;DR

This paper introduces a minimal tensor-network model inspired by physics to capture local phonetic correlations in speech, reducing information content and aiding in learning and generating plausible new words.

Contribution

It presents a novel minimal model of phonetic memory using tensor networks that captures local correlations to improve speech learning and error prediction.

Findings

Model successfully learns Latin and Turkish words

Enables generation of phonetically plausible new words

Provides hierarchy of likely speech errors

Abstract

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.)