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

Autor/inn/en	Semenzin, Chiara; Hamrick, Lisa; Seidl, Amanda; Kelleher, Bridgette L.; Cristia, Alejandrina
Titel	Describing Vocalizations in Young Children: A Big Data Approach through Citizen Science Annotation
Quelle	In: Journal of Speech, Language, and Hearing Research, 64 (2021) 7, S.2401-2416 (16 Seiten) PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Cristia, Alejandrina)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1092-4388
Schlagwörter	Young Children; Audio Equipment; Documentation; Speech; Validity; Interrater Reliability; Expertise; Classification; Citizen Participation; Scientific Research; Genetic Disorders; Language Impairments + Suchen Sie Ihr Suchwort? Frühe Kindheit; Audio-CD; Dokumentation; Speaking; Sprechen; Gültigkeit; Interrater-Reliabilität; Expert appraisal; Classification system; Klassifikation; Klassifikationssystem; 'Citizen participation; Citizens'' participation'; Bürgerbeteiligung; Speech disorder; Speech disorders; Speech disabilities; Speech disability; Speech handicap; Speech handicaps; Speech impairment; Speech impairments; Language handicaps; Sprachbehinderung
Abstract	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. (As Provided).
Anmerkungen	American Speech-Language-Hearing Association. 2200 Research Blvd #250, Rockville, MD 20850. Tel: 301-296-5700; Fax: 301-296-8580; e-mail: slhr@asha.org; Web site: http://jslhr.pubs.asha.org
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01