AudVowelConsNet: A phoneme-level based deep CNN architecture for clinical depression diagnosis

Muhammad Muzammel; Hanan Salam; Yann Hoffmann; Mohamed Chetouani; Alice Othmani

doi:10.1016/j.mlwa.2020.100005

Journal Articles Machine Learning with Applications Year : 2020

AudVowelConsNet: A phoneme-level based deep CNN architecture for clinical depression diagnosis

, , , , (1, 2)

1
2

Muhammad Muzammel

Function : Author

Hanan Salam

Function : Author

Yann Hoffmann

Function : Author

Mohamed Chetouani

Function : Author

Alice Othmani

Function : Author
PersonId : 2660
IdHAL : alice-ahlem-othmani
ORCID : 0000-0002-3442-0578
IdRef : 182870375

Laboratoire Images, Signaux et Systèmes Intelligents

SYNAPSE

CV

Domains

Computer Science [cs]

Lab Synapse : Connect in order to contact the contributor

https://hal.u-pec.fr/hal-04320359

Submitted on : Monday, December 4, 2023-10:13:52 AM

Last modification on : Tuesday, January 9, 2024-3:33:55 PM

Dates and versions

hal-04320359 , version 1 (04-12-2023)

Identifiers

HAL Id : hal-04320359 , version 1
ARXIV : 2010.16201
DOI : 10.1016/j.mlwa.2020.100005

Cite

Muhammad Muzammel, Hanan Salam, Yann Hoffmann, Mohamed Chetouani, Alice Othmani. AudVowelConsNet: A phoneme-level based deep CNN architecture for clinical depression diagnosis. Machine Learning with Applications, 2020, 2, pp.100005. ⟨10.1016/j.mlwa.2020.100005⟩. ⟨hal-04320359⟩

Export

BibTeX XML-TEI Dublin Core DC Terms EndNote DataCite

Collections

LISSI UPEC

16 View

0 Download

AudVowelConsNet: A phoneme-level based deep CNN architecture for clinical depression diagnosis

Domains

Dates and versions

Identifiers

Cite

Export

Collections

Altmetric

Share