English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

Depression detection using vocal biomarkers is a highly researched area.Articulatory coordination features (ACFs) are developed based on the changes inneuromotor coordination due to psychomotor slowing, a key feature of MajorDepressive Disorder. However findings of existing studies are mostly validatedon a single database which limits the generalizability of results. Variabilityacross different depression databases adversely affects the results in crosscorpus evaluations (CCEs). We propose to develop a generalized classifier fordepression detection using a dilated Convolutional Neural Network which istrained on ACFs extracted from two depression databases. We show that ACFsderived from Vocal Tract Variables (TVs) show promise as a robust set offeatures for depression detection. Our model achieves relative accuracyimprovements of ~10% compared to CCEs performed on models trained on a singledatabase. We extend the study to show that fusing TVs and Mel-FrequencyCepstral Coefficients can further improve the performance of this classifier.

Generalized Dilated CNN Models for Depression Detection Using Inverted Vocal Tract Variables