Using an Automated Speech Recognition Approach to Differentiate Between Normal and Aspirating Swallowing Sounds Recorded from Digital Cervical Auscultation in Children

Use of machine learning to accurately detect aspirating swallowing sounds in children is an evolving field. Previously reported classifiers for the detection of aspirating swallowing sounds in children have reported sensitivities between 79 and 89%. This study aimed to investigate the accuracy of us...

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Veröffentlicht in:	Dysphagia 2022-12, Vol.37 (6), p.1482-1492
Hauptverfasser:	Frakking, Thuy T., Chang, Anne B., Carty, Christopher, Newing, Jade, Weir, Kelly A., Schwerin, Belinda, So, Stephen
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Auscultation Auscultation - methods Child Children Deglutition Deglutition Disorders - diagnosis Dysphagia Female Gastroenterology Hepatology Humans Imaging Learning algorithms Machine learning Male Medical colleges Medical imaging equipment Medicine Medicine & Public Health Original Original Article Otorhinolaryngology Radiology Sound Speech Perception Speech recognition Support vector machines Swallowing
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