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High-probability grants
According to our matching algorithm, Anne S. Warlaumont is the likely recipient of the following grants.
Years |
Recipients |
Code |
Title / Keywords |
Matching score |
2015 — 2018 |
Kello, Christopher Gopinathan, Ajay (co-PI) [⬀] Warlaumont, Anne |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Compcog: Infant Vocalization as Foraging For Caregiver Responses @ University of California - Merced
During the first year of life, infants begin to vocalize, and their cooing and babbling become increasingly more sophisticated and complex, paving the way for speech. Infants' vocalizations often occur in strings or clusters of similar-sounding vocalizations. Caregivers frequently respond to babies' vocalizations even before they can talk, and adults vary their responses depending on the types of vocalization babies produce. These adult responses appear to influence babies' subsequent vocalizations. This research offers a new explanatory perspective on infant vocalization by borrowing ideas from what we know about how animals forage, systematically exploring an environment to find food. When foraging, animals strike a balance between revisiting familiar locations where food has been found and trying out new locations that may lead to undiscovered resources. This project will advance understanding of how infants refine their vocal abilities by considering prelinguistic infants as foragers who explore the range of possible vocalizations in search of interesting or useful feedback, including positive responses from adults.
Daylong home audio recordings will be collected from English- and Spanish-learning infants at 3, 6, 9, and 18 months of age. Infant vocalizations will be automatically identified and analyzed acoustically, then situated in an abstract acoustic information space. For each region of acoustic space, the likelihood of receiving an adult response will be ascertained. Methods previously developed to analyze patterns of foraging for resources in physical space will be applied. Adult vocalization acoustics and their relationship to infant vocal foraging responses will be similarly analyzed. Computational models of infant and adult vocal foraging will be employed to explore possible mechanisms that underlie the observed temporal dynamics of infant and adult vocalizations over the course of the day. Vocal foraging measures will be tested for changes across age and with socioeconomic status. Whether foraging patterns during the first year predict speech-language abilities at 18 months will also be tested. The work has the potential to unveil new candidates for automatically obtainable markers that could be useful for early identification of children at risk for speech-language delays or disorders.
|
0.915 |
2015 — 2019 |
Warlaumont, Anne |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Ridir: Collaborative Research: Enabling Access to and Analysis of Shared Daylong Child and Family Audio Data @ University of California - Merced
A child's language development in the first few years of life predicts long-term cognitive development, academic achievement, and expected income as an adult. Early language development in turn depends on linguistic interactions with adults. Increasingly, researchers are using daylong audio recordings to study child language development and child-caregiver interactions. Compared to short language samples, daylong recordings capture the full range of experiences a child has over the course of a day. Daylong audio recordings are also being used in applied settings. For example, studies show that by the time they enter First Grade, children from higher socioeconomic backgrounds hear tens of millions more words than children from lower socioeconomic backgrounds, perpetuating social inequalities. Multiple large-scale intervention projects targeting low socioeconomic households, including the Thirty Million Words Initiative in Chicago and the Providence Talks program, are using daylong audio recordings to provide automated, personalized feedback to parents on when and how often their child hears adult words and experiences conversational turns. The features of daylong recordings that are advantageous for researchers and practitioners also pose unique challenges. For one, their long durations are ideal for studying the temporal dynamics of child-adult interaction, but taking advantage of the long durations requires the enlistment of automated speech recognition technology. Current automatic speech recognition systems have difficulties with child speech and are challenged by the noisy and varied acoustic environments represented in the recordings. Another challenge is that the recordings capture private moments that require long hours of human listening to remove. This makes it difficult for researchers to share the recordings publicly, so that the potential value of the recordings collected by individual research labs is not fully realized.
This project will create a new resource, called HomeBank, that will have three key components: (1) a public dataset containing daylong audio recordings that have had private information removed by human listeners, (2) a larger dataset containing about ten to one hundred times as many hours of recording that have not had private information removed and will be free but restricted to those who have demonstrated training in human research ethics, and (3) an open-source repository of computer programs to automatically analyze the daylong audio recordings. HomeBank will take advantage of an existing cyberinfrastructure for sharing linguistic data called TalkBank. The daylong audio recordings included in the datasets will represent both typically developing and clinical groups, a range of ages from newborn infants to school age children, and a range of language and socioeconomic backgrounds. We expect the primary users to be basic and applied child development researchers as well as engineers developing automatic speech recognition technologies. The free-to-access database and the open source computer programs will ultimately improve both the data on which early interventions are based and the tools available for providing parents with feedback on the linguistic input they provide their children.
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0.915 |