How effective is LENA in detecting speech vocalizations and language produced by children and adolescents with ASD in...

The LENA system was designed and validated to provide information about the language environment in children 0 to 4 years of age and its use has been expanded to populations with a number of communication profiles. Its utility in children 5 years of age and older is not yet known. The present study used acoustic data from two samples of children with autism spectrum disorders (ASD) to evaluate the reliability of LENA automated analyses for detecting speech utterances in older, school age children, and adolescents with ASD, in clinic and home environments. Participants between 5 and 18 years old who were minimally verbal (study 1) or had a range of verbal abilities (study 2) completed standardized assessments in the clinic (study 1 and 2) and in the home (study 2) while speech was recorded from a LENA device. We compared LENA segment labels with manual ground truth coding by human transcribers using two different methods. We found that the automated LENA algorithms were not successful (<50% reliable) in detecting vocalizations from older children and adolescents with ASD, and that the proportion of speaker misclassifications by the automated system increased significantly with the target-child's age. The findings in children and adolescents with ASD suggest possibly misleading results when expanding the use of LENA beyond the age ranges for which it was developed and highlight the need to develop novel automated methods that are more appropriate for older children. Autism Research 2019, 12: 628-635. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: Current commercially available speech detection algorithms (LENA system) were previously validated in toddlers and children up to 48 months of age, and it is not known whether they are reliable in older children and adolescents. Our data suggest that LENA does not adequately capture speech in school age children and adolescents with autism and highlights the need to develop new automated methods for older children.