This paper is in the following e-collection/theme issue:

Web-based and Mobile Health Interventions (3345) Mobile Health (mhealth) (2974) Depression and Mood Disorders; Suicide Prevention (1387) Machine Learning (1491) Text-messaging (SMS, WeChat etc)-Based Interventions (657) Speech and Phonetics in Verbal Protocols (Think Aloud, Recall, and Explanation Tasks) (93)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/34474, first published .
Automatic Depression Detection Using Smartphone-Based Text-Dependent Speech Signals: Deep Convolutional Neural Network Approach

Automatic Depression Detection Using Smartphone-Based Text-Dependent Speech Signals: Deep Convolutional Neural Network Approach

Automatic Depression Detection Using Smartphone-Based Text-Dependent Speech Signals: Deep Convolutional Neural Network Approach

Authors of this article:

Ah Young Kim1 Author Orcid Image ;   Eun Hye Jang1 Author Orcid Image ;   Seung-Hwan Lee2, 3, 4 Author Orcid Image ;   Kwang-Yeon Choi5 Author Orcid Image ;   Jeon Gue Park6, 7 Author Orcid Image ;   Hyun-Chool Shin8 Author Orcid Image
Article Authors Cited by (21) Tweetations (6) Metrics

Journals

  1. Guo K, Xiao Y, Deng W, Zhao G, Zhang J, Liang Y, Yang L, Liao G. Speech disorders in patients with Tongue squamous cell carcinoma: A longitudinal observational study based on a questionnaire and acoustic analysis. BMC Oral Health 2023;23(1) View
  2. Ksibi A, Zakariah M, Menzli L, Saidani O, Almuqren L, Hanafieh R. Electroencephalography-Based Depression Detection Using Multiple Machine Learning Techniques. Diagnostics 2023;13(10):1779 View
  3. Shekar P, Mathew A, Yeswanth P, Deivalakshmi S. A combined deep CNN-RNN network for rainfall-runoff modelling in Bardha Watershed, India. Artificial Intelligence in Geosciences 2024;5:100073 View
  4. Beniwal R, Saraswat P. A Hybrid BERT-CNN Approach for Depression Detection on Social Media Using Multimodal Data. The Computer Journal 2024;67(7):2453 View
  5. Humayun M, Shuja J, Abas P. A review of social background profiling of speakers from speech accents. PeerJ Computer Science 2024;10:e1984 View
  6. Shin J, Bae S. Use of voice features from smartphones for monitoring depressive disorders: Scoping review. DIGITAL HEALTH 2024;10 View
  7. Siegel J, Cohen A, Szabo S, Tomioka S, Opler M, Kirkpatrick B, Hopkins S. Enrichment using speech latencies improves treatment effect size in a clinical trial of bipolar depression. Psychiatry Research 2024;340:116105 View
  8. Miyata S. Integration of basic and clinical researches to develop the biomarker of ‍depression. Folia Pharmacologica Japonica 2024;159(5):311 View
  9. Beniwal R, Saraswat P. A hybrid BERT-CPSO model for multi-class depression detection using pure hindi and hinglish multimodal data on social media. Computers and Electrical Engineering 2024;120:109786 View
  10. Li X, Dong Y, Yi Y, Liang Z, Yan S. Hypergraph Neural Network for Multimodal Depression Recognition. Electronics 2024;13(22):4544 View
  11. Almutairi S, Abohashrh M, Razzaq H, Zulqarnain M, Namoun A, Khan F. A Hybrid Deep Learning Model for Predicting Depression Symptoms From Large-Scale Textual Dataset. IEEE Access 2024;12:168477 View

Books/Policy Documents

  1. Verma R, Kumar G, Yadav A. Proceedings of International Conference on Recent Innovations in Computing. View