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.)

翻译：口语演化受制于言语经济性，这取决于人类口腔结构等因素，从而在口语词汇中产生局部语音相关性。本文证明这些局部相关性通过降低信息内容促进口语词汇的学习。我们借鉴用于多体物理的类似变分模型，构建了局部连接的张量网络模型，利用这些局部语音相关性来辅助口语词汇学习。因此该模型是音位记忆的最小模型，其中"学习发音"与"学习词汇"是同一过程。其结果是能够习得产生目标语言中语音合理的新词汇的能力，同时提供言语行为中可能产生的最常见错误层级结构。我们使用拉丁语和土耳其语词汇对该模型进行了验证（代码已发布在GitHub上）。