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

Speech emotion recognition systems (SER) can achieve high accuracy when thetraining and test data are identically distributed, but this assumption isfrequently violated in practice and the performance of SER systems plummetagainst unforeseen data shifts. The design of robust models for accurate SER ischallenging, which limits its use in practical applications. In this paper wepropose a deeper neural network architecture wherein we fuse DenseNet, LSTM andHighway Network to learn powerful discriminative features which are robust tonoise. We also propose data augmentation with our network architecture tofurther improve the robustness. We comprehensively evaluate the architecturecoupled with data augmentation against (1) noise, (2) adversarial attacks and(3) cross-corpus settings. Our evaluations on the widely used IEMOCAP andMSP-IMPROV datasets show promising results when compared with existing studiesand state-of-the-art models.

Deep Architecture Enhancing Robustness to Noise, Adversarial Attacks, and Cross-corpus Setting for Speech Emotion Recognition