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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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Data-driven automated acoustic analysis of human infant vocalizations using neural network tools
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Abstract:
Acoustic analysis of infant vocalizations has typically employed traditional acoustic measures drawn from adult speech acoustics, such as f0, duration, formant frequencies, amplitude, and pitch perturbation. Here an alternative and complementary method is proposed in which data-derived spectrographic features are central. 1-s-long spectrograms of vocalizations produced by six infants recorded longitudinally between ages 3 and 11 months are analyzed using a neural network consisting of a self-organizing map and a single-layer perceptron. The self-organizing map acquires a set of holistic, data-derived spectrographic receptive fields. The single-layer perceptron receives self-organizing map activations as input and is trained to classify utterances into prelinguistic phonatory categories (squeal, vocant, or growl), identify the ages at which they were produced, and identify the individuals who produced them. Classification performance was significantly better than chance for all three classification tasks. Performance is compared to another popular architecture, the fully supervised multilayer perceptron. In addition, the network’s weights and patterns of activation are explored from several angles, for example, through traditional acoustic measurements of the network’s receptive fields. Results support the use of this and related tools for deriving holistic acoustic features directly from infant vocalization data and for the automatic classification of infant vocalizations.
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Keyword:
Speech Production [70]
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URL: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2865706 https://doi.org/10.1121/1.3327460 http://www.ncbi.nlm.nih.gov/pubmed/20370038
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