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 Tools annual

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 monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

As a neurophysiological response to threat or adverse conditions, stress canaffect cognition, emotion and behaviour with potentially detrimental effects onhealth in the case of sustained exposure. Since the affective content of speechis inherently modulated by an individual's physical and mental state, asubstantial body of research has been devoted to the study of paralinguisticcorrelates of stress-inducing task load. Historically, voice stress analysis(VSA) has been conducted using conventional digital signal processing (DSP)techniques. Despite the development of modern methods based on deep neuralnetworks (DNNs), accurately detecting stress in speech remains difficult due tothe wide variety of stressors and considerable variability in the individualstress perception. To that end, we introduce a set of five datasets for taskload detection in speech. The voice recordings were collected as eithercognitive or physical stress was induced in the cohort of volunteers, with acumulative number of more than a hundred speakers. We used the datasets todesign and evaluate a novel self-supervised audio representation that leveragesthe effectiveness of handcrafted features (DSP-based) and the complexity ofdata-driven DNN representations. Notably, the proposed approach outperformedboth extensive handcrafted feature sets and novel DNN-based audiorepresentation learning approaches.

Hybrid Handcrafted and Learnable Audio Representation for Analysis of Speech Under Cognitive and Physical Load