SAYCam: A Large, Longitudinal Audiovisual Dataset Recorded From the Infant’s Perspective

By Jessica Sullivan, Michelle Mei, Andrew Perfors, Erica H Wojcik, Michael C. Frank. 2020

138 citations recorded by the source corpus.

Abstract We introduce a new resource: the SAYCam corpus. Infants aged 6–32 months wore a head-mounted camera for approximately 2 hr per week, over the course of approximately two-and-a-half years. The result is a large, naturalistic, longitudinal dataset of infant- and child-perspective videos. Over 200,000 words of naturalistic speech have already been transcribed. Similarly, the dataset is searchable using a number of criteria (e.g., age of participant, location, setting, objects present). The resulting dataset will be of broad use to psychologists, linguists, and computer scientists.

Subfields: Cognitive Neuroscience, Developmental and Educational Psychology, Multimodal Learning, Cognitive Science & Psychology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

1. Timothy P. Lillicrap
2. Adam Santoro
3. Felix Hill
4. Nikolay Savinov
5. Aurelia Guy
6. Greg Wayne
7. Joshua P. Neunuebel
8. James J. Knierim

In Developmental and Educational Psychology

1. Timothy P. Lillicrap
2. Stephen Clark
3. Felix Hill
4. Adam Santoro
5. Greg Wayne

In Multimodal Learning

1. Felix Hill
2. Adam Santoro

In Cognitive Science & Psychology

1. Stephen Clark
2. Felix Hill
3. Timothy P. Lillicrap
4. Adam Santoro
5. Greg Wayne

Citations in the corpus

• Imitating Interactive Intelligence (2020, positive): “Following Yoshida and Smith (2008), Sullivan et al. (2020) recorded a large dataset of audio-visual experience from head-cameras on children aged 6-32 months.”