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End-to-End Model-Based Detection of Infants with Autism Spectrum Disorder Using a Pretrained Model

In this paper, we propose an end-to-end (E2E) neural network model to detect autism spectrum disorder (ASD) from children’s voices without explicitly extracting the deterministic features. In order to obtain the decisions for discriminating between the voices of children with ASD and those with typi...

Ausführliche Beschreibung

Gespeichert in:
Hauptverfasser: Jung Hyuk Lee, Geon Woo Lee, Guiyoung Bong, Hee Jeong Yoo, Hong Kook Kim
Format: Artigo
Sprache:Inglês
Veröffentlicht: MDPI AG 2022-12-01
Schriftenreihe:Sensors
Schlagworte:
autism spectrum disorder
end-to-end neural network
pretrained model
joint optimization
autoencoder
bidirectional long short-term memory (BLSTM)
Online-Zugang:https://www.mdpi.com/1424-8220/23/1/202
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