Employing speech and location information for automatic assessment of child language environments

Assessment of the language environment of children in early childhood is a challenging task for both human and machine, and understanding the classroom environment of early learners is an essential step towards facilitating language acquisition and development. This paper explores an approach for intelligent language environment monitoring based on the duration of child-to-child and adult-to-child conversations and a child's physical location in classrooms within a childcare center. The amount of child's communication with other children and adults was measured using an i-vector based child-adult diarization system (developed at CRSS). Furthermore the average time spent by each child across different activity areas within the classroom was measured using a location tracking system. The proposed solution here offers unique opportunities to assess speech and language interaction for children, and quantify location context which would contribute to improved language environments.