Use of the LENA Autism Screen with Children who are Deaf or Hard of Hearing

Abstract

Background and Objectives: This systematic review reports the evidence from the literature concerning the potential for using an automated vocal analysis, the Language ENvironment Analysis (LENA, LENA Research Foundation, Boulder, CO, USA) in the screening process for children at risk for autism spectrum disorder (ASD) and deaf or hard of hearing (D/HH). ASD and D/HH have increased comorbidity, but current behavioral diagnostic and screening tools have limitations. The LENA Language Autism Screen (LLAS) may offer an additional tool to disambiguate ASD from D/HH in young children. Materials and Methods: We examine empirical reports that use automatic vocal analysis methods to differentiate disordered from typically developing children. Results: Consensus across the sampled scientific literature shows support for use of automatic methods for screening and disambiguation of children with ASD and D/HH. There is some evidence of vocal differentiation between ASD, D/HH, and typically-developing children warranting use of the LLAS, but additional empirical evidence is needed to better understand the strengths and weaknesses of the tool. Conclusions: The findings reported here warrant further, more substantive, methodologically-sound research that is fully powered to show a reliable difference. Findings may be useful for both clinicians and researchers in better identification and understanding of communication disorders.

Adapting and validating the Autism Diagnostic Observation Schedule Version 2 for use with deaf children and young people

“…During the DIEP there was considerable discussion about the use of the item that coded for use of intonation in Module 1. Whilst some DIEP members thought this coding would be a poor discriminator between ASD and those without ASD in deaf children because deaf children’s intonation is frequently reported as different from hearing children (Mora et al, 2012 ) emerging research under discussion on the DIEP suggested that vocal analysis and intonation could discriminate between hearing children, deaf children and children with ASD (VanDam & Yoshinaga-Itano, 2019 ). For this reason the DIEP and IRRT agreed to leave the original coding and noted that information gleaned from using this coding in future practice or research could yield important information about differences between groups.…”

Using big data from long-form recordings to study development and optimize societal impact

“…Furthermore, current algorithms (the LENA pipeline), as well as those under continued development such as the ACLEW pipeline, allow for effortless characterization of infant vocal quantity and quality (i.e., vocal maturity and complexity) (Oller et al, 2010;Seidl et al, 2018;Yoder, Oller, Richards, Gray, & Gilkerson, 2013). The continued development of automated signal processing techniques will garner additional information about the developmental profiles of neuro-diverse infants and children, hopefully enabling clinicians to make more fine-grained distinctions between disorders, such as ASD, fragile X, and hearing loss, that have similar developmental profiles in infancy (VanDam & Yoshinaga-Itano, 2019), but clearly very different treatment profiles. In addition, the accumulation of long-form datasets may give us enough training data to develop metrics for more nuanced disorders like developmental language disorder and stuttering, for which no infant vocal markers have been postulated (perhaps due to the limited data that can be analyzed with traditional clinical approaches).…”

Describing Vocalizations in Young Children: A Big Data Approach Through Citizen Science Annotation

“…Some research suggests that spontaneous behavior, captured for instance via home videos, could provide important indices to development (Belardi et al, 2017;Overby et al, 2020). Advancements in the field of wearable technologies, such as Language ENvironment Analysis (LENA) recorders, have opened new avenues to both early detection of speech pathologies and research in language development more generally (Oller et al, 2010;Rankine et al, 2017;VanDam & Yoshinaga-Itano, 2019). Wearable recorders allow data collection to happen in the child's natural environment, and at a large scale, which may be particularly helpful for children whose speech is not easily elicited.…”