As a neurophysiological response to threat or adverse conditions, stress can
affect cognition, emotion and behaviour with potentially detrimental effects on
health in the case of sustained exposure. Since the affective content of speech
is inherently modulated by an individual's physical and mental state, a
substantial body of research has been devoted to the study of paralinguistic
correlates 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 neural
networks (DNNs), accurately detecting stress in speech remains difficult due to
the wide variety of stressors and considerable variability in the individual
stress perception. To that end, we introduce a set of five datasets for task
load detection in speech. The voice recordings were collected as either
cognitive or physical stress was induced in the cohort of volunteers, with a
cumulative number of more than a hundred speakers. We used the datasets to
design and evaluate a novel self-supervised audio representation that leverages
the effectiveness of handcrafted features (DSP-based) and the complexity of
data-driven DNN representations. Notably, the proposed approach outperformed
both extensive handcrafted feature sets and novel DNN-based audio
representation learning approaches.

本文提出了一种使用手工特征和基于数据驱动深度神经网络表示的自监督音频表示方法，用于检测语音中负荷相关的压力。此方法在多个数据集中展现优异性能，胜过传统数字信号处理方法和最新的基于深度神经网络的声音表示学习方法。