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

Most works on Affective Signal Processing (ASP) focus on user-dependent emotion recognition models  which are personalized to a specific subject. As these types of approach have good accuracy rates, they  cannot easily be reused with other subjects for industrial or research purposes. On the other hand, the  reported accuracy rates of user-independent models are substantially lower. This performance decrease is  mostly due to the greater variance in the physiological training data set drawn from multiple users. In this  paper, we propose an approach to address this problem and enhance the performance of user-independent  models by explicitly modeling subjectsâ idiosyncrasies. As a first exemplification, we describe how  personality traits can be used to improve the accuracy of user-independent emotion recognition models. We  also present the experiment that will be carried on to validate the proposed approach.

Addressing Subject-dependency for Affective Signal Processing - Modeling Subjects’ Idiosyncracies