Automatic
speech processing (ASP) has recently been applied to very large datasets of
naturalistically collected, daylong recordings of child speech via an audio
recorder worn by young children. The system developed by the LENA Research
Foundation analyzes children’s speech for research and clinical purposes,
with special focus on of identifying and tagging family speech dynamics and
the at-home acoustic environment from the auditory perspective of the child.
A primary issue for researchers, clinicians, and families using the Language
ENvironment Analysis (LENA) system is to what degree the segment labels are
valid. This classification study evaluates the performance of the computer
ASP output against 23 trained human judges who made about 53,000 judgements
of classification of segments tagged by the LENA ASP. Results indicate
performance consistent with modern ASP such as those using HMM methods, with
acoustic characteristics of fundamental frequency and segment duration most
important for both human and machine classifications. Results are likely to
be important for interpreting and improving ASP output.
