The human speechome project

  • Authors:

  • Deb Roy;Rupal Patel;Philip DeCamp;Rony Kubat;Michael Fleischman;Brandon Roy;Nikolaos Mavridis;Stefanie Tellex;Alexia Salata;Jethran Guinness;Michael Levit;Peter Gorniak

  • Affiliations:

  • Cognitive Machines Group, MIT Media Laboratory;Communication Analysis and Design Laboratory, Northeastern University;Cognitive Machines Group, MIT Media Laboratory;Cognitive Machines Group, MIT Media Laboratory;Cognitive Machines Group, MIT Media Laboratory;Cognitive Machines Group, MIT Media Laboratory;Cognitive Machines Group, MIT Media Laboratory;Cognitive Machines Group, MIT Media Laboratory;Cognitive Machines Group, MIT Media Laboratory;Cognitive Machines Group, MIT Media Laboratory;Cognitive Machines Group, MIT Media Laboratory;Cognitive Machines Group, MIT Media Laboratory

  • Venue:
  • EELC'06 Proceedings of the Third international conference on Emergence and Evolution of Linguistic Communication: symbol Grounding and Beyond
  • Year:
  • 2006

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Abstract

The Human Speechome Project is an effort to observe and computationally model the longitudinal course of language development for a single child at an unprecedented scale. We are collecting audio and video recordings for the first three years of one child's life, in its near entirety, as it unfolds in the child's home. A network of ceiling-mounted video cameras and microphones are generating approximately 300 gigabytes of observational data each day from the home. One of the worlds largest single-volume disk arrays is under construction to house approximately 400,000 hours of audio and video recordings that will accumulate over the three year study. To analyze the massive data set, we are developing new data mining technologies to help human analysts rapidly annotate and transcribe recordings using semi-automatic methods, and to detect and visualize salient patterns of behavior and interaction. To make sense of large-scale patterns that span across months or even years of observations, we are developing computational models of language acquisition that are able to learn from the childs experiential record. By creating and evaluating machine learning systems that step into the shoes of the child and sequentially process long stretches of perceptual experience, we will investigate possible language learning strategies used by children with an emphasis on early word learning.