2023
DOI: 10.1049/sil2.12207
|View full text |Cite
|
Sign up to set email alerts
|

Identification of depression state based on multi‐scale acoustic features in interrogation environment

Abstract: Depression diagnosis based on speech signals has the advantages of non-invasiveness, low cost, and few restrictions on portability. The research on the recognition of the depression state is carried out based on the acoustic information in the speech signal. Aiming at the interview dialogue speech in the consultation environment, a hierarchical attention temporal convolutional network (HATCN) acoustic depression recognition model is proposed. For sentence acoustic feature learning, a regional attention mechani… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

0
0
0

Year Published

2023
2023
2024
2024

Publication Types

Select...
3

Relationship

0
3

Authors

Journals

citations
Cited by 3 publications
references
References 32 publications
0
0
0
Order By: Relevance