The formation of perceptual space in early phonetic acquisition: a cross-linguistic modeling approach
This research addresses the problem of understanding early phonetic learning mechanisms for developmental linguistics and computational modeling, though it is incremental as it builds on previous studies.
The study investigated how perceptual space is organized in early phonetic acquisition by training autoencoder models on English and Mandarin without contextual cues, finding that unsupervised training led to similar representations in native and non-native conditions, resembling infant universal listening.
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.