The formation of perceptual space in early phonetic acquisition: a cross-linguistic modeling approach

TL;DR

This study models early phonetic learning across languages using autoencoders to understand how perceptual space and categories form in infants without contextual cues.

Contribution

It introduces a cross-linguistic autoencoder modeling approach to simulate early phonetic acquisition focusing on perceptual space formation.

Findings

Unsupervised training yields similar perceptual representations for native and non-native conditions.

Models trained on English and Mandarin show early universal listening patterns.

Insights into how infants organize phonetic categories without contextual information.

Abstract

This study investigates how learners organize perceptual space in early phonetic acquisition by advancing previous studies in two key aspects. Firstly, it examines the shape of the learned hidden representation as well as its ability to categorize phonetic categories. Secondly, it explores the impact of training models on context-free acoustic information, without involving contextual cues, on phonetic acquisition, closely mimicking the early language learning stage. Using a cross-linguistic modeling approach, autoencoder models are trained on English and Mandarin and evaluated in both native and non-native conditions, following experimental conditions used in infant language perception studies. The results demonstrate that unsupervised bottom-up training on context-free acoustic information leads to comparable learned representations of perceptual space between native and non-native conditions for both English and Mandarin, resembling the early stage of universal listening in infants. These findings provide insights into the organization of perceptual space during early phonetic acquisition and contribute to our understanding of the formation and representation of phonetic categories.