Enhancing accuracy and privacy in speech-based depression detection through speaker disentanglement-Reference-Cited by-同舟云学术

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Enhancing accuracy and privacy in speech-based depression detection through speaker disentanglement

Published:2024-06 Issue: Volume:86 Page:101605
ISSN:0885-2308
Container-title:Computer Speech & Language
language:en
Short-container-title:Computer Speech & Language

Author:

Ravi Vijay^ORCID,Wang Jinhan,Flint Jonathan,Alwan Abeer

Funder

National Institutes of Health

Publisher

Elsevier BV

Subject

Human-Computer Interaction,Theoretical Computer Science,Software

Reference86 articles.

1. Computer-aided diagnosis of depression using EEG signals;Acharya;Eur. Neurol.,2015

2. Effectiveness of voice quality features in detecting depression;Afshan,2018

3. Detecting depression: a comparison between spontaneous and read speech;Alghowinem,2013

4. Linguistic analysis of speech in affective disorders;Andreasen;Arch. Gen. Psychiatry,1976

5. NetVLAD: CNN architecture for weakly supervised place recognition;Arandjelovic,2016

Cited by 5 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Enhancing multimodal depression detection with intra- and inter-sample contrastive learning;Information Sciences;2024-12

2. Speechformer-CTC: Sequential modeling of depression detection with speech temporal classification;Speech Communication;2024-09

3. Diagnostic accuracy of deep learning using speech samples in depression: a systematic review and meta-analysis;Journal of the American Medical Informatics Association;2024-07-16

4. Privacy-Oriented Manipulation of Speaker Representations;IEEE Access;2024

5. Speechformer-Ctc: Sequential Modeling of Depression Detection with Speech Temporal Classification;2024

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