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Do Infants Really Learn Phonetic Categories?
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In: EISSN: 2470-2986 ; Open Mind ; https://hal.archives-ouvertes.fr/hal-03550830 ; Open Mind, MIT Press, 2021, 5, pp.113-131. ⟨10.1162/opmi_a_00046⟩ (2021)
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Early phonetic learning without phonetic categories -- Insights from large-scale simulations on realistic input
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In: ISSN: 0027-8424 ; EISSN: 1091-6490 ; Proceedings of the National Academy of Sciences of the United States of America ; https://hal.archives-ouvertes.fr/hal-03070566 ; Proceedings of the National Academy of Sciences of the United States of America , National Academy of Sciences, 2021, 118 (7), pp.e2001844118. ⟨10.1073/pnas.2001844118⟩ (2021)
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Abstract:
International audience ; Before they even speak, infants become attuned to the sounds of the language(s) they hear, processing native phonetic contrasts more easily than non-native ones. For example, between 6-8 months and 10-12 months, infants learning American English get better at distinguishing English [ɹ] and [l], as in ‘rock’ vs ‘lock’, relative to infants learning Japanese. Influential accounts of this early phonetic learning phenomenon initially proposed that infants group sounds into native vowel- and consonant-like phonetic categories—like [ɹ] and [l] in English—through a statistical clustering mechanism dubbed ‘distributional learning’. The feasibility of this mechanism for learning phonetic categories has been challenged, however. Here we demonstrate that a distributional learning algorithm operating on naturalistic speech can predict early phonetic learning as observed in Japanese and American English infants, suggesting that infants might learn through distributional learning after all. We further show, however, that contrary to the original distributional learning proposal, our model learns units too brief and too fine-grained acoustically to correspond to phonetic categories. This challenges the influential idea that what infants learn are phonetic categories. More broadly, our work introduces a novel mechanism-driven approach to the study of early phonetic learning, together with a quantitative modeling framework that can handle realistic input. This allows, for the first time, accounts of early phonetic learning to be linked to concrete, systematic predictions regarding infants’ attunement.
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Keyword:
[INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]; [SCCO]Cognitive science
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URL: https://doi.org/10.1073/pnas.2001844118
https://hal.archives-ouvertes.fr/hal-03070566/document
https://hal.archives-ouvertes.fr/hal-03070566/file/full_article.pdf
https://hal.archives-ouvertes.fr/hal-03070566
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| 3 |
Making Heads or Tails of it: A Competition–Compensation Account of Morphological Deficits in Language Impairment
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In: Proceedings of the Annual Meeting of the Cognitive Science Society, vol 43, iss 43 (2021)
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| 5 |
Making Heads or Tails of it: A Competition–Compensation Account of Morphological Deficits in Language Impairment ...
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| 6 |
Do Infants Really Learn Phonetic Categories?
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In: Open Mind (Camb) (2021)
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| 7 |
Early phonetic learning without phonetic categories: Insights from large-scale simulations on realistic input
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In: Proc Natl Acad Sci U S A (2021)
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| 9 |
Evaluating computational models of infant phonetic learning across languages ...
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| 10 |
Modeling the Learning of the Person Case Constraint
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In: Proceedings of the Society for Computation in Linguistics (2020)
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| 11 |
Normalization may be ineffective for phonetic category learning ...
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| 13 |
How to use context for phonetic learning and perception from naturalistic speech
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| 14 |
Normalization may be ineffective for phonetic category learning
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In: Proceedings of the Society for Computation in Linguistics (2019)
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| 15 |
Young infants’ discrimination of subtle phonetic contrasts
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In: ISSN: 0010-0277 ; EISSN: 1873-7838 ; Cognition ; https://hal.archives-ouvertes.fr/hal-01841528 ; Cognition, Elsevier, 2018, 178, pp.57 - 66. ⟨10.1016/j.cognition.2018.05.009⟩ (2018)
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| 19 |
Establishing New Mappings between Familiar Phones: Neural and Behavioral Evidence for Early Automatic Processing of Nonnative Contrasts
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| 20 |
Computational modeling of the role of discourse information in language production and language acquisition
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