From Sound Primitives Learning to Structure Extraction: Brain-inspired Model of Infant Early Vocal Learning - Archive ouverte HAL
Communication Dans Un Congrès Année : 2023

From Sound Primitives Learning to Structure Extraction: Brain-inspired Model of Infant Early Vocal Learning

Résumé

The underlying mechanism of the biological neural machinery of the human brain receives an upsurge of attention from machine learning scientists, as developmental learning gives insight into open-ended unsupervised learning. In particular, language acquisition remains an exploitable field in not only machine learning but psychology and biology as well. Psychologically speaking, infants’ perception of speech sounds in early infancy contributes to later language acquisition, making it one of the main paths of exploring how infants’ brains manage to process early information. It is noticeable that adults’ perception of speech sounds becomes constrained compared to that of newborns, as influenced by the phonetic patterns of their native language, which appears by the end of the first year after birth. Correspondingly, in machine learning’s perspective, it is doubtful if unsupervised learning in adulthood might happen without supervised learning. Thus developmental learning until infants’ first anniversary is believed to be the most similar mechanism to unsupervised machine learning. As a result, our research focuses on modeling computationally the learning mechanism of speech sound in early infancy, more precisely before 12 months of age, and we propose a bio-inspired early-infant-based neural network for the learning of speech sounds, which lays a foundation for the later grammatical structure extraction neural network for language acquisition.
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Dates et versions

hal-04372439 , version 1 (04-01-2024)

Identifiants

  • HAL Id : hal-04372439 , version 1

Citer

Xiaodan Chen, Alexandre Pitti, Mathias Quoy. From Sound Primitives Learning to Structure Extraction: Brain-inspired Model of Infant Early Vocal Learning. IMOL, Stéphane Doncieux, Sep 2023, Paris Sorbonne Université, France. ⟨hal-04372439⟩
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