13 research outputs found

    A Social Feedback Loop for Speech Development and Its Reduction in Autism

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    We analyzed the microstructure of child-adult interaction during naturalistic, daylong, automatically labeled audio recordings (13,836 hr total) of children (8- to 48-month-olds) with and without autism. We found that an adult was more likely to respond when the child\u27s vocalization was speech related rather than not speech related. In turn, a child\u27s vocalization was more likely to be speech related if the child\u27s previous speech-related vocalization had received an immediate adult response rather than no response. Taken together, these results are consistent with the idea that there is a social feedback loop between child and caregiver that promotes speech development. Although this feedback loop applies in both typical development and autism, children with autism produced proportionally fewer speech-related vocalizations, and the responses they received were less contingent on whether their vocalizations were speech related. We argue that such differences will diminish the strength of the social feedback loop and have cascading effects on speech development over time. Differences related to socioeconomic status are also reported. © The Author(s) 2014

    Adult responses to infant prelinguistic vocalizations are associated with infant vocabulary: A home observation study.

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    This study used LENA recording devices to capture infants' home language environments and examine how qualitative differences in adult responding to infant vocalizations related to infant vocabulary. Infant-directed speech and infant vocalizations were coded in samples taken from daylong home audio recordings of 13-month-old infants. Infant speech-related vocalizations were identified and coded as either canonical or non-canonical. Infant-directed adult speech was identified and classified into different pragmatic types. Multiple regressions examined the relation between adult responsiveness, imitating, recasting, and expanding and infant canonical and non-canonical vocalizations with caregiver-reported infant receptive and productive vocabulary. An interaction between adult like-sound responding (i.e., the total number of imitations, recasts, and expansions) and infant canonical vocalizations indicated that infants who produced more canonical vocalizations and received more adult like-sound responses had higher productive vocabularies. When sequences were analyzed, infant canonical vocalizations that preceded and followed adult recasts and expansions were positively associated with infant productive vocabulary. These findings provide insights into how infant-adult vocal exchanges are related to early vocabulary development

    A Social Feedback Loop for Speech Development and Its Reduction in Autism

    No full text
    We analyzed the microstructure of child-adult interaction during naturalistic, daylong, automatically labeled audio recordings (13,836 hr total) of children (8- to 48-month-olds) with and without autism. We found that an adult was more likely to respond when the child\u27s vocalization was speech related rather than not speech related. In turn, a child\u27s vocalization was more likely to be speech related if the child\u27s previous speech-related vocalization had received an immediate adult response rather than no response. Taken together, these results are consistent with the idea that there is a social feedback loop between child and caregiver that promotes speech development. Although this feedback loop applies in both typical development and autism, children with autism produced proportionally fewer speech-related vocalizations, and the responses they received were less contingent on whether their vocalizations were speech related. We argue that such differences will diminish the strength of the social feedback loop and have cascading effects on speech development over time. Differences related to socioeconomic status are also reported. © The Author(s) 2014

    Adult responses to infant prelinguistic vocalizations are associated with infant vocabulary: A home observation study.

    No full text
    This study used LENA recording devices to capture infants' home language environments and examine how qualitative differences in adult responding to infant vocalizations related to infant vocabulary. Infant-directed speech and infant vocalizations were coded in samples taken from daylong home audio recordings of 13-month-old infants. Infant speech-related vocalizations were identified and coded as either canonical or non-canonical. Infant-directed adult speech was identified and classified into different pragmatic types. Multiple regressions examined the relation between adult responsiveness, imitating, recasting, and expanding and infant canonical and non-canonical vocalizations with caregiver-reported infant receptive and productive vocabulary. An interaction between adult like-sound responding (i.e., the total number of imitations, recasts, and expansions) and infant canonical vocalizations indicated that infants who produced more canonical vocalizations and received more adult like-sound responses had higher productive vocabularies. When sequences were analyzed, infant canonical vocalizations that preceded and followed adult recasts and expansions were positively associated with infant productive vocabulary. These findings provide insights into how infant-adult vocal exchanges are related to early vocabulary development

    The INTERSPEECH 2017 computational paralinguistics challenge:addressee, cold & snoring

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    Abstract The INTERSPEECH 2017 Computational Paralinguistics Challenge addresses three different problems for the first time in research competition under well-defined conditions: In the Addressee sub-challenge, it has to be determined whether speech produced by an adult is directed towards another adult or towards a child; in the Cold sub-challenge, speech under cold has to be told apart from ‘healthy’ speech; and in the Snoring sub-challenge, four different types of snoring have to be classified. In this paper, we describe these sub-challenges, their conditions, and the baseline feature extraction and classifiers, which include data-learnt feature representations by end-to-end learning with convolutional and recurrent neural networks, and bag-of-audio-words for the first time in the challenge series
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