Can A Signing Virtual Human Engage A Baby'S Attention?

PROCEEDINGS OF THE 19TH ACM INTERNATIONAL CONFERENCE ON INTELLIGENT VIRTUAL AGENTS (IVA' 19)(2019)

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摘要
The child developmental period of ages 6-12 months marks a widely understood "critical period" for healthy language learning, during which, failure to receive exposure to language can place babies at risk for language and reading problems spanning life. Deaf babies constitute one vulnerable population as they can experience dramatically reduced or no access to usable linguistic input during this period. Technology has been used to augment linguistic input (e.g., auditory devices; language videotapes) but research finds limitations in learning. We evaluated an AI system that uses an Avatar (provides language and socially contingent interactions) and a robot (aids attention to the Avatar) to facilitate infants' ability to learn aspects of American Sign Language (ASL), and asked three questions: (1) Can babies with little/no exposure to ASL distinguish among the Avatar's different conversational modes (Linguistic Nursery Rhymes; Social Gestures; Idle/nonlinguistic postures; 3rd person observer)? (2) Can an Avatar stimulate babies' production of socially contingent responses, and crucially, nascent language responses? (3) What is the impact of parents' presence/absence of conversational participation? Surprisingly, babies (i) spontaneously distinguished among Avatar conversational modes, (ii) produced varied socially contingent responses to Avatar's modes, and (iii) parents influenced an increase in babies' response tokens to some Avatar modes, but the overall categories and pattern of babies' behavioral responses remained proportionately similar irrespective of parental participation. Of note, babies produced the greatest percentage of linguistic responses to the Avatar's Linguistic Nursery Rhymes versus other Avatar conversational modes. This work demonstrates the potential for Avatars to facilitate language learning in young babies.
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Empirical Studies, Social Impact, Multi-agent Interaction
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