Minimal Effective Theory for Phonotactic Memory: Capturing Local Correlations due to Errors in Speech
This provides a minimal model of phonetic memory for understanding speech evolution and learning, but it is incremental as it builds on existing tensor-network methods from physics.
The paper tackles the problem of how local phonetic correlations in spoken words facilitate learning by reducing information content, using a tensor-network model to show that learning pronunciation and word learning are unified, enabling generation of phonetically reasonable new words and predicting likely speech errors.
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.)