Automatic depression recognition by intelligent speech signal processing: A systematic survey
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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-09-01
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| Series: | CAAI Transactions on Intelligence Technology |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/cit2.12113 |
| _version_ | 1797682967552720896 |
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| author | Pingping Wu Ruihao Wang Han Lin Fanlong Zhang Juan Tu Miao Sun |
| author_facet | Pingping Wu Ruihao Wang Han Lin Fanlong Zhang Juan Tu Miao Sun |
| author_sort | Pingping Wu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-12T00:07:34Z |
| format | Article |
| id | doaj.art-ddfacf95832f499b988e061224768401 |
| institution | Directory Open Access Journal |
| issn | 2468-2322 |
| language | English |
| last_indexed | 2024-03-12T00:07:34Z |
| publishDate | 2023-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | CAAI Transactions on Intelligence Technology |
| spelling | doaj.art-ddfacf95832f499b988e0612247684012023-09-16T16:19:34ZengWileyCAAI Transactions on Intelligence Technology2468-23222023-09-018370171110.1049/cit2.12113Automatic depression recognition by intelligent speech signal processing: A systematic surveyPingping Wu0Ruihao Wang1Han Lin2Fanlong Zhang3Juan Tu4Miao Sun5Jiangsu Key Laboratory of Public Project Audit, School of Engineering Audit Nanjing Audit University Nanjing ChinaSchool of Information Engineering Nanjing Audit University Nanjing ChinaJiangsu Key Laboratory of Public Project Audit, School of Engineering Audit Nanjing Audit University Nanjing ChinaSchool of Information Engineering Nanjing Audit University Nanjing ChinaKey Laboratory of Modern Acoustics (MOE), School of Physics Nanjing University Nanjing ChinaFaculty of Electrical Engineering, Mathematics & Computer Science Delft University of Technology Delft The NetherlandsAbstract 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.https://doi.org/10.1049/cit2.12113acoustic signal processingdeep learningfeature extractionspeech depression recognition |
| spellingShingle | Pingping Wu Ruihao Wang Han Lin Fanlong Zhang Juan Tu Miao Sun Automatic depression recognition by intelligent speech signal processing: A systematic survey CAAI Transactions on Intelligence Technology acoustic signal processing deep learning feature extraction speech depression recognition |
| title | Automatic depression recognition by intelligent speech signal processing: A systematic survey |
| title_full | Automatic depression recognition by intelligent speech signal processing: A systematic survey |
| title_fullStr | Automatic depression recognition by intelligent speech signal processing: A systematic survey |
| title_full_unstemmed | Automatic depression recognition by intelligent speech signal processing: A systematic survey |
| title_short | Automatic depression recognition by intelligent speech signal processing: A systematic survey |
| title_sort | automatic depression recognition by intelligent speech signal processing a systematic survey |
| topic | acoustic signal processing deep learning feature extraction speech depression recognition |
| url | https://doi.org/10.1049/cit2.12113 |
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