Open AccessAutomatic depression recognition by intelligent speech signal processing: A systematic survey
Author(s) -
Wu Pingping,
Wang Ruihao,
Lin Han,
Zhang Fanlong,
Tu Juan,
Sun Miao
Publication year - 2023
Publication title -
caai transactions on intelligence technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.613
H-Index - 15
ISSN - 2468-2322
DOI - 10.1049/cit2.12113
Subject(s) - feature extraction , computer science , speech recognition , key (lock) , feature (linguistics) , signal (programming language) , artificial intelligence , depression (economics) , signal processing , machine learning , pattern recognition (psychology) , telecommunications , linguistics , philosophy , radar , computer security , programming language , economics , macroeconomics
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.