Communication Dans Un Congrès Année : 2024

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

Résumé

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.
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Dates et versions

hal-04737568 , version 1 (22-12-2024)

Identifiants

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Xiaodan Chen, Alexandre Pitti, Mathias Quoy, Nancy F Chen. Developmental Predictive Coding Model for Early Infancy Mono and Bilingual Vocal Continual Learning. Artificial Neural Networks and Machine Learning – ICANN 2024, Sep 2024, Lugano, Switzerland. pp.16 - 32, ⟨10.1007/978-3-031-72350-6_2⟩. ⟨hal-04737568⟩
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