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Modeling early phonetic acquisition from child-centered audio data.
Lavechin, Marvin; de Seyssel, Maureen; Métais, Marianne; Metze, Florian; Mohamed, Abdelrahman; Bredin, Hervé; Dupoux, Emmanuel; Cristia, Alejandrina.
Afiliación
  • Lavechin M; Laboratoire de Sciences Cognitives et Psycholinguistique, Département d'Etudes Cognitives, ENS, EHESS, CNRS, PSL University, Paris, France; Cognitive Machine Learning Team, INRIA, Paris, France; Meta AI Research, Paris, France. Electronic address: marvinlavechin@gmail.com.
  • de Seyssel M; Laboratoire de Sciences Cognitives et Psycholinguistique, Département d'Etudes Cognitives, ENS, EHESS, CNRS, PSL University, Paris, France; Cognitive Machine Learning Team, INRIA, Paris, France; Laboratoire de linguistique formelle, Université de Paris, CNRS, Paris, France.
  • Métais M; Laboratoire de Sciences Cognitives et Psycholinguistique, Département d'Etudes Cognitives, ENS, EHESS, CNRS, PSL University, Paris, France; Cognitive Machine Learning Team, INRIA, Paris, France.
  • Metze F; Meta AI Research, Paris, France.
  • Mohamed A; Rembrand, Palo Alto, CA, United States.
  • Bredin H; Institut de Recherche en Informatique de Toulouse, Université de Toulouse, CNRS, Toulouse, France.
  • Dupoux E; Laboratoire de Sciences Cognitives et Psycholinguistique, Département d'Etudes Cognitives, ENS, EHESS, CNRS, PSL University, Paris, France; Cognitive Machine Learning Team, INRIA, Paris, France; Meta AI Research, Paris, France.
  • Cristia A; Laboratoire de Sciences Cognitives et Psycholinguistique, Département d'Etudes Cognitives, ENS, EHESS, CNRS, PSL University, Paris, France; Cognitive Machine Learning Team, INRIA, Paris, France.
Cognition ; 245: 105734, 2024 04.
Article en En | MEDLINE | ID: mdl-38335906
ABSTRACT
Infants learn their native language(s) at an amazing speed. Before they even talk, their perception adapts to the language(s) they hear. However, the mechanisms responsible for this perceptual attunement and the circumstances in which it takes place remain unclear. This paper presents the first attempt to study perceptual attunement using ecological child-centered audio data. We show that a simple prediction algorithm exhibits perceptual attunement when applied on unrealistic clean audio-book data, but fails to do so when applied on ecologically-valid child-centered data. In the latter scenario, perceptual attunement only emerges when the prediction mechanism is supplemented with inductive biases that force the algorithm to focus exclusively on speech segments while learning speaker-, pitch-, and room-invariant representations. We argue these biases are plausible given previous research on infants and non-human animals. More generally, we show that what our model learns and how it develops through exposure to speech depends exquisitely on the details of the input signal. By doing so, we illustrate the importance of considering ecologically valid input data when modeling language acquisition.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Percepción del Habla / Fonética Tipo de estudio: Prognostic_studies Límite: Humans / Infant Idioma: En Revista: Cognition Año: 2024 Tipo del documento: Article Pais de publicación: Países Bajos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Percepción del Habla / Fonética Tipo de estudio: Prognostic_studies Límite: Humans / Infant Idioma: En Revista: Cognition Año: 2024 Tipo del documento: Article Pais de publicación: Países Bajos