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Low-dimensional representation of infant and adult vocalization acoustics ...
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Developing a Cross-Cultural Annotation System and MetaCorpus for Studying Infants’ Real World Language Experience
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Adult responses to infant prelinguistic vocalizations are associated with infant vocabulary: A home observation study.
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In: PloS one, vol 15, iss 11 (2020)
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What Do North American Babies Hear? A large-scale cross-corpus analysis.
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The INTERSPEECH 2019 computational paralinguistics challenge: Styrian dialects, continuous sleepiness, baby sounds & orca activity
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Language Origins Viewed in Spontaneous and Interactive Vocal Rates of Human and Bonobo Infants
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What Do North American Babies Hear? A large-scale cross-corpus analysis
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The early emergence and puzzling decline of relational reasoning: Effects of knowledge and search on inferring abstract concepts.
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The early emergence and puzzling decline of relational reasoning: Effects of knowledge and search on inferring abstract concepts.
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HomeBank: An Online Repository of Daylong Child-Centered Audio Recordings.
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Learning to Produce Syllabic Speech Sounds via Reward-Modulated Neural Plasticity
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HomeBank: An Online Repository of Daylong Child-Centered Audio Recordings
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Functional flexibility of infant vocalization and the emergence of language
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Prespeech motor learning in a neural network using reinforcement☆
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Abstract:
Vocal motor development in infancy provides a crucial foundation for language development. Some significant early accomplishments include learning to control the process of phonation (the production of sound at the larynx) and learning to produce the sounds of one’s language. Previous work has shown that social reinforcement shapes the kinds of vocalizations infants produce. We present a neural network model that provides an account of how vocal learning may be guided by reinforcement. The model consists of a self-organizing map that outputs to muscles of a realistic vocalization synthesizer. Vocalizations are spontaneously produced by the network. If a vocalization meets certain acoustic criteria, it is reinforced, and the weights are updated to make similar muscle activations increasingly likely to recur. We ran simulations of the model under various reinforcement criteria and tested the types of vocalizations it produced after learning in the differ-ent conditions. When reinforcement was contingent on the production of phonated (i.e. voiced) sounds, the network’s post learning productions were almost always phonated, whereas when reinforcement was not contingent on phonation, the network’s post-learning productions were almost always not phonated. When reinforcement was contingent on both phonation and proximity to English vowels as opposed to Korean vowels, the model’s post-learning productions were more likely to resemble the English vowels and vice versa.
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Keyword:
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URL: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3541464
http://www.ncbi.nlm.nih.gov/pubmed/23275137
https://doi.org/10.1016/j.neunet.2012.11.012
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Data-driven automated acoustic analysis of human infant vocalizations using neural network tools
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