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

Representation learning for speech emotion recognition is challenging due tolabeled data sparsity issue and lack of gold standard references. In addition,there is much variability from input speech signals, human subjectiveperception of the signals and emotion label ambiguity. In this paper, wepropose a machine learning framework to obtain speech emotion representationsby limiting the effect of speaker variability in the speech signals.Specifically, we propose to disentangle the speaker characteristics fromemotion through an adversarial training network in order to better representemotion. Our method combines the gradient reversal technique with an entropyloss function to remove such speaker information. Our approach is evaluated onboth IEMOCAP and CMU-MOSEI datasets. We show that our method improves speechemotion classification and increases generalization to unseen speakers.

Speaker-invariant Affective Representation Learning via Adversarial Training