Sound Mind: Detecting Depression and Anxiety in Humans
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-19-7402-1_19
Reference21 articles.
1. Ding Y, Chen X, Fu Q, Zhong S (2020) A depression recognition method for college students utilizing deep integrated support vector algorithm. IEEE Access 8:75616–75629. https://doi.org/10.1109/ACCESS.2020.2987523
2. Kiss G, Jenei AZ (2020) Investigation of the accuracy of depression prediction based on speech processing, 2020 43rd international conference on telecommunications and signal processing (TSP), pp 129–132. https://doi.org/10.1109/TSP49548.2020.9163495
3. Jenei AZ, Kiss G (2020) Possibilities of recognizing depression with convolutional networks applied in correlation structure. In: 2020 43rd international conference on telecommunications and signal processing (TSP), pp 101–104. https://doi.org/10.1109/TSP49548.2020.9163547
4. An H, Lu X, Shi D, Yuan J, Li R, Pan T (2019) Mental health detection from speech signal: a convolution neural networks approach. In: 2019 international joint conference on information, media and engineering (IJCIME), pp 436–439. https://doi.org/10.1109/IJCIME49369.2019.00094
5. Huang K, Wu C, Su M, Chou C (2017) Mood disorder identification using deep bottleneck features of elicited speech. In: 2017 Asia-Pacific signal and information processing association annual summit and conference (APSIPAASC), pp 1648–1652. https://doi.org/10.1109/APSIPA.2017.8282296
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