Automated depression analysis using convolutional neural networks from speech

Journal of Biomedical Informatics
Lang He, Cui Cao

Abstract

To help clinicians to efficiently diagnose the severity of a person's depression, the affective computing community and the artificial intelligence field have shown a growing interest in designing automated systems. The speech features have useful information for the diagnosis of depression. However, manually designing and domain knowledge are still important for the selection of the feature, which makes the process labor consuming and subjective. In recent years, deep-learned features based on neural networks have shown superior performance to hand-crafted features in various areas. In this paper, to overcome the difficulties mentioned above, we propose a combination of hand-crafted and deep-learned features which can effectively measure the severity of depression from speech. In the proposed method, Deep Convolutional Neural Networks (DCNN) are firstly built to learn deep-learned features from spectrograms and raw speech waveforms. Then we manually extract the state-of-the-art texture descriptors named median robust extended local binary patterns (MRELBP) from spectrograms. To capture the complementary information within the hand-crafted features and deep-learned features, we propose joint fine-tuning layers to combine the ra...Continue Reading

Citations

Jun 14, 2020·Translational Psychiatry·Chang SuFei Wang
Jul 28, 2020·Nature Reviews. Rheumatology·Daniel H Solomon, Robert S Rudin
Sep 5, 2019·The New England Journal of Medicine·Ida Sim
Oct 13, 2020·Clinical EEG and Neuroscience·Yousef Mohammadi, Mohammad Hassan Moradi
Apr 2, 2021·Clinical Pharmacology and Therapeutics·Demissie AlemayehuSatrajit Roychoudhury

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