Developmental Predictive Coding Model for Early Infancy Mono and Bilingual Vocal Continual Learning

TL;DR

This paper introduces a small, interpretable neural model with predictive coding for early bilingual speech learning, highlighting the importance of critical periods and online learning in language acquisition.

Contribution

It presents a novel, interpretable neural network model that simulates infant speech learning and bilingual acquisition using continual learning and predictive coding mechanisms.

Findings

Model replicates perceptual narrowing in second language learning during later infancy.

Online learning enhances adaptability and responsiveness to new speech data.

Bilingual learning during critical periods shows advantages over later infancy acquisition.

Abstract

Understanding how infants perceive speech sounds and language structures is still an open problem. Previous research in artificial neural networks has mainly focused on large dataset-dependent generative models, aiming to replicate language-related phenomena such as ''perceptual narrowing''. In this paper, we propose a novel approach using a small-sized generative neural network equipped with a continual learning mechanism based on predictive coding for mono-and bilingual speech sound learning (referred to as language sound acquisition during ''critical period'') and a compositional optimization mechanism for generation where no learning is involved (later infancy sound imitation). Our model prioritizes interpretability and demonstrates the advantages of online learning: Unlike deep networks requiring substantial offline training, our model continuously updates with new data, making it adaptable and responsive to changing inputs. Through experiments, we demonstrate that if second language acquisition occurs during later infancy, the challenges associated with learning a foreign language after the critical period amplify, replicating the perceptual narrowing effect.