Objective speech intelligibility prediction using a deep learning model with continuous speech-evoked cortical auditory responses.

Na, Youngmin; Joo, Hyosung; Trang, Le Thi; Quan, Luong Do Anh; Woo, Jihwan

Na, Youngmin; Joo, Hyosung; Trang, Le Thi; Quan, Luong Do Anh; Woo, Jihwan.

Afiliação

Na Y; Department of Biomedical Engineering, University of Ulsan, Ulsan, South Korea.
Joo H; Department of Electrical, Electronic and Computer Engineering, University of Ulsan, Ulsan, South Korea.
Trang LT; Department of Electrical, Electronic and Computer Engineering, University of Ulsan, Ulsan, South Korea.
Quan LDA; Department of Electrical, Electronic and Computer Engineering, University of Ulsan, Ulsan, South Korea.
Woo J; Department of Biomedical Engineering, University of Ulsan, Ulsan, South Korea.

Front Neurosci ; 16: 906616, 2022.

Article em En | MEDLINE | ID: mdl-36061597

Palavras-chave

EEG; continuous speech; deep-learning; occlusion sensitivity; speech intelligibility

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Front Neurosci Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Coréia do Sul

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