🧩 Performance variations were driven more by participants' speech patterns than by diagnostic groups, with the amount of other children's vocalizations, female and male adult speech predicting the performance of both algorithms.

9/12

⚖️ Despite being trained exclusively on typically-developing children, both algorithms maintained consistent performance across all diagnostic groups (low-risk, Angelman, fragile X, Down, siblings of children with ASD)!

8/12

⚖️ What about automatic counts: CTC, AWC, CVC?
Both algorithms capture a large portion of variance in human counts (Pearson's r from .78 to .92)

ACLEW seems better on CTC (.92 vs .83)
LENA is slightly better on AWC (.82 vs .78)
ACLEW is slightly better on CVC (.88 vs .83)

7/12

⚖️ We found very different segmentation strategies.
For every 100 hours of speech, LENA correctly classified 45 hours, missed 41 hours, generated 27 hours of false alarms, and confused the speaker category for 14 hours.

LENA makes few mistakes but misses a lot of speech.

5/12

🤖 Both algorithms start by segmenting the audio into speaker categories: key child, other children, male and female adult. From this segmentation step, they extract key metrics: Conversational Turn Count (CTC), Adult Word Count (AWC), and Child Vocalization Count (CVC).

3/12