Automatic depression recognition by intelligent speech signal processing

Title

Automatic depression recognition by intelligent speech signal processing: A systematic survey

 Author

Wu, Pingping (Nanjing Audit University)
Wang, Ruihao (Nanjing Audit University)
Lin, Han (Nanjing Audit University)
Zhang, Fanlong (Nanjing Audit University)
Tu, Juan (Nanjing University)
Sun, M. (TU Delft Signal Processing Systems)

Date

2022

 Abstract

Depression has become one of the most common mental illnesses in the world. For better prediction and diagnosis, methods of automatic depression recognition based on speech signal are constantly proposed and updated, with a transition from the early traditional methods based on hand-crafted features to the application of architectures of deep learning. This paper systematically and precisely outlines the most prominent and up-to-date research of automatic depression recognition by intelligent speech signal processing so far. Furthermore, methods for acoustic feature extraction, algorithms for classification and regression, as well as end to end deep models are investigated and analysed. Finally, general trends are summarised and key unresolved issues are identified to be considered in future studies of automatic speech depression recognition.


 To reference this document use:

http://resolver.tudelft.nl/uuid:ded518a4-3538-4f0a-9a59-8e598bbc578e

 DOI

https://doi.org/10.1049/cit2.12113

 ISSN

2468-6557

 Source

CAAI Transactions on Intelligence Technology, 8 (3), 701-711

 Part of collection

Institutional Repository

 Document type

review

 Rights

© 2022 Pingping Wu, Ruihao Wang, Han Lin, Fanlong Zhang, Juan Tu, M. Sun
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