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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We present a framework to identify whether a public speaker's body movementsare meaningful or non-meaningful ("Mannerisms") in the context of theirspeeches. In a dataset of 84 public speaking videos from 28 individuals, weextract 314 unique body movement patterns (e.g. pacing, gesturing, shiftingbody weights, etc.). Online workers and the speakers themselves annotated themeaningfulness of the patterns. We extracted five types of features from theaudio-video recordings: disfluency, prosody, body movements, facial, andlexical. We use linear classifiers to predict the annotations with AUC up to0.82. Analysis of the classifier weights reveals that it puts larger weights onthe lexical features while predicting self-annotations. Contrastingly, it putsa larger weight on prosody features while predicting audience annotations. Thisanalysis might provide subtle hint that public speakers tend to focus more onthe verbal features while evaluating self-performances. The audience, on theother hand, tends to focus more on the non-verbal aspects of the speech. Thedataset and code associated with this work has been released for peer reviewand further analysis.

Automatic Identification of Non-Meaningful Body-Movements and What It Reveals About Humans