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

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