Automatic measurement and analysis of the child verbal communication using classroom acoustics within a child care center

Understanding the language environment of early learners is a challenging task for both human and machine, and it is critical in facilitating effective language development among young children. This papers presents a new application for the existing diarization systems and investigates the language environment of young children using a turn taking strategy employing an i-vector based baseline that captures adult-to-child or child-tochild conversational turns across different classrooms in a child care center. Detecting speaker turns is necessary before more in depth subsequent analysis of audio such as word count, speech recognition, and keyword spotting which can contribute to the design of future learning spaces specifically designed for typically developing children, or those at-risk with communication limitations. Experimental results using naturalistic childteacher classroom settings indicate the proposed rapid childadult speech turn taking scheme is highly effective under noisy classroom conditions and results in 27.3% relative error rate reduction compared to the baseline results produced by the LIUM diarization toolkit.