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

The Myth of Women's Advantage in Using Child-Directed Speech: Evidence of Women Versus Men in Single-Sex-Parent Families

PURPOSE

Differences between child-directed speech (CDS) by women and men are generally explained by either biological-evolutionary or gender-social theories. It is difficult to tease these two explanations apart for different-sex-parent families because women are usually also the main caregivers. Thus, this study aims to examine the influence of parental sex on CDS by investigating men and women who are in same-sex-parent families.

METHOD

Twenty same-sex-parent families participated in the study-10 families in which the parents were two men and 10 families in which the parents were two women. The families were matched for toddler age (range: 9-24 months) and sex. CDS was recorded using the Language Environment Analysis (LENA) device for 16 hr during a day. Each parent was also audio-recorded during a 30-min play session with his or her child.

RESULTS

No difference was found between men and women across all the LENA measures, namely, adult word count, conversational turns count, and child vocalization count. The analysis of speech samples during parent-child play showed no difference between men and women in mean length of utterance and number of nouns, verbs, and adjectives. Pragmatic speech acts of initiations, responses to infants' actions, or vocalizations were similar in both sexes. Women used more "teaching" utterances than men, and men who were main caregivers used more "teaching" utterances than men who were secondary caregivers. Across both sexes, secondary caregivers used more "requests for actions" compared to main caregivers.

CONCLUSIONS

The present findings support a functional-social approach and not a biological approach for explaining the use of CDS by men and women. These findings have clinical implications on the involvement of men in early intervention programs.

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

Big data are everywhere. In this chapter, we focus on one source: long-form, child-centered recordings collected using wearable technologies. Because these recordings are simultaneously unobtrusive and encompassing, they may be a breakthrough technology for clinicians and researchers from several diverse fields. We demonstrate this possibility by outlining three applications for the recordings-clinical treatment, large-scale interventions, and language documentation-where we see the greatest potential. We argue that incorporating these recordings into basic and applied research will result in more equitable treatment of patients, more reliable measurements of the effects of interventions on real-world behavior, and deeper scientific insights with less observational bias. We conclude by outlining a proposal for a semistructured online platform where vast numbers of long-form recordings could be hosted and more representative, less biased algorithms could be trained.

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

Purpose Recording young children's vocalizations through wearables is a promising method to assess language development. However, accurately and rapidly annotating these files remains challenging. Online crowdsourcing with the collaboration of citizen scientists could be a feasible solution. In this article, we assess the extent to which citizen scientists' annotations align with those gathered in the lab for recordings collected from young children. Method Segments identified by Language ENvironment Analysis as produced by the key child were extracted from one daylong recording for each of 20 participants: 10 low-risk control children and 10 children diagnosed with Angelman syndrome, a neurogenetic syndrome characterized by severe language impairments. Speech samples were annotated by trained annotators in the laboratory as well as by citizen scientists on Zooniverse. All annotators assigned one of five labels to each sample: Canonical, Noncanonical, Crying, Laughing, and Junk. This allowed the derivation of two child-level vocalization metrics: the Linguistic Proportion and the Canonical Proportion. Results At the segment level, Zooniverse classifications had moderate precision and recall. More importantly, the Linguistic Proportion and the Canonical Proportion derived from Zooniverse annotations were highly correlated with those derived from laboratory annotations. Conclusions Annotations obtained through a citizen science platform can help us overcome challenges posed by the process of annotating daylong speech recordings. Particularly when used in composites or derived metrics, such annotations can be used to investigate early markers of language delays.

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

We report a Delphi Consensus modification and first validation study of the Autism Diagnostic Observation Schedule - 2 with deaf children and young people (ADOS-2 Deaf adaptation). Validation included 122 deaf participants (aged 2-18 years), 63 with an Autism Spectrum Disorder (ASD). This was compared to a National Institute for Health and Clinical Excellence (NICE) guideline standard clinical assessment by blinded independent specialist clinicians. Results showed overall sensitivity 73% (95%CI 60%, 83%); specificity 71% (95%CI 58%, 82%), and for the more common modules 1-3 (combined as in previous studies) sensitivity 79% (95% CI 65-89%); specificity 79% (95% CI 66-89%) suggesting this instrument will be a helpful addition for use with deaf children and young people.