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/

Global: Zendy Plus annual

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

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Conversations contain a wide spectrum of multimodal information that gives ushints about the emotions and moods of the speaker. In this paper, we developeda system that supports humans to analyze conversations. Our main contributionis the identification of appropriate multimodal features and the integration ofsuch features into verbatim conversation transcripts. We demonstrate theability of our system to take in a wide range of multimodal information andautomatically generated a prediction score for the depression state of theindividual. Our experiments showed that this approach yielded betterperformance than the baseline model. Furthermore, the multimodal narrativeapproach makes it easy to integrate learnings from other disciplines, such asconversational analysis and psychology. Lastly, this interdisciplinary andautomated approach is a step towards emulating how practitioners record thecourse of treatment as well as emulating how conversational analysts have beenanalyzing conversations by hand.

Detecting depression in dyadic conversations with multimodal narratives and visualizations