Speech@FIT, Faculty of Information Technology, Brno University of Technology, Brno, Czech Republic.
Department of Psychology, Faculty of Arts, Masaryk University, Brno, Czech Republic.
Sci Data. 2024 Nov 12;11(1):1221. doi: 10.1038/s41597-024-03991-w.
Early identification of cognitive or physical overload is critical in fields where human decision making matters when preventing threats to safety and property. Pilots, drivers, surgeons, and operators of nuclear plants are among those affected by this challenge, as acute stress can impair their cognition. In this context, the significance of paralinguistic automatic speech processing increases for early stress detection. The intensity, intonation, and cadence of an utterance are examples of paralinguistic traits that determine the meaning of a sentence and are often lost in the verbatim transcript. To address this issue, tools are being developed to recognize paralinguistic traits effectively. However, a data bottleneck still exists in the training of paralinguistic speech traits, and the lack of high-quality reference data for the training of artificial systems persists. Regarding this, we present an original empirical dataset collected using the BESST experimental protocol for capturing speech signals under induced stress. With this data, our aim is to promote the development of pre-emptive intervention systems based on stress estimation from speech.
早期识别认知或身体超负荷在人类决策对安全和财产构成威胁的领域至关重要。飞行员、驾驶员、外科医生和核电厂操作人员都受到这一挑战的影响，因为急性应激会损害他们的认知能力。在这种情况下，副语言自动语音处理对于早期压力检测的重要性增加。言语的强度、语调和谐振是决定句子意义的副语言特征的示例，而这些特征在逐字记录中经常丢失。为了解决这个问题，人们正在开发工具来有效识别副语言特征。然而，在训练副语言语音特征方面仍然存在数据瓶颈，并且缺乏用于训练人工智能系统的高质量参考数据。关于这一点，我们提出了一个原始的经验数据集，该数据集是使用 BESST 实验协议收集的，用于在诱导压力下捕获语音信号。有了这个数据，我们的目标是促进基于语音压力估计的先发干预系统的发展。
