Sulek, Smith, Bent, Hudry, Trembath, Vivanti, Dissanayake
Background:
There is growing understanding of the potential benefits of a multi-method
approach to accurately capture language skills of children on the autism
spectrum. Tools such as Language ENvironment Analysis (LENA) provide an
efficient means of capturing and analysing early child vocalizations (CVs)
and the language learning environment. While developed to capture whole-day
recordings of child language in naturalistic settings, there is potential
utility in capturing, but little knowledge about, primary LENA
metrics—including CVs and conversational turns (CTs)—and novel metrics, such
as vocalization ratios (VRs), sampled in clinical practice settings where
children are often seen. Moreover, recent research indicates that the novel
VR may offer a broad indicator of children’s developmental level, beyond just
their language abilities, a hypothesis yet to be investigated in a large
sample of children for whom the LENA was designed (i.e., pre-schoolers).
