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

As a common way of emotion signaling via non-linguistic vocalizations, vocalburst (VB) plays an important role in daily social interaction. Understandingand modeling human vocal bursts are indispensable for developing robust andgeneral artificial intelligence. Exploring computational approaches forunderstanding vocal bursts is attracting increasing research attention. In thiswork, we propose a hierarchical framework, based on chain regression models,for affective recognition from VBs, that explicitly considers multiplerelationships: (i) between emotional states and diverse cultures; (ii) betweenlow-dimensional (arousal & valence) and high-dimensional (10 emotion classes)emotion spaces; and (iii) between various emotion classes within thehigh-dimensional space. To address the challenge of data sparsity, we also useself-supervised learning (SSL) representations with layer-wise and temporalaggregation modules. The proposed systems participated in the ACII AffectiveVocal Burst (A-VB) Challenge 2022 and ranked first in the "TWO'' and "CULTURE''tasks. Experimental results based on the ACII Challenge 2022 datasetdemonstrate the superior performance of the proposed system and theeffectiveness of considering multiple relationships using hierarchicalregression chain models.

A Hierarchical Regression Chain Framework for Affective Vocal Burst Recognition