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

Emotions recognition is commonly employed for health assessment. However, thetypical metric for evaluation in therapy is based on patient-doctor appraisal.This process can fall into the issue of subjectivity, while also requiringhealthcare professionals to deal with copious amounts of information. Thus,machine learning algorithms can be a useful tool for the classification ofemotions. While several models have been developed in this domain, there is alack of userfriendly representations of the emotion classification systems fortherapy. We propose a tool which enables users to take speech samples andidentify a range of emotions (happy, sad, angry, surprised, neutral, clam,disgust, and fear) from audio elements through a machine learning model. Thedashboard is designed based on local therapists' needs for intuitiverepresentations of speech data in order to gain insights and informativeanalyses of their sessions with their patients.

Speech-Based Emotion Recognition using Neural Networks and Information Visualization