English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that oftenemerges in early childhood. ASD assessment typically involves an observationprotocol including note-taking and ratings of child's social behavior conductedby a trained clinician. A robust machine learning (ML) model that is capable oflabeling adult and child audio has the potential to save significant time andlabor in manual coding children's behaviors. This may assist clinicians captureevents of interest, better communicate events with parents, and educate newclinicians. In this study, we leverage the self-supervised learning model,Wav2Vec 2.0 (W2V2), pretrained on 4300h of home recordings of children under 5years old, to build a unified system that performs both speaker diarization(SD) and vocalization classification (VC) tasks. We apply this system totwo-channel audio recordings of brief 3-5 minute clinician-child interactionsusing the Rapid-ABC corpus. We propose a novel technique by introducingauxiliary features extracted from W2V2-based automatic speech recognition (ASR)system for children under 4 years old to improve children's VC task. We testour proposed method of improving children's VC task on two corpora (Rapid-ABCand BabbleCor) and observe consistent improvements. Furthermore, we reach, orperhaps outperform, the state-of-the-art performance of BabbleCor.

Enhancing Child Vocalization Classification in Multi-Channel Child-Adult Conversations Through Wav2vec2 Children ASR Features