Schewski Lilien, Doss Mathew Magimai, Beldi Guido, Keller Sandra
Department for Biomedical Research (DBMR), University of Bern, Bern, Switzerland.
Department for Visceral Surgery and Medicine, Bern University Hospital, University of Bern, Bern, Switzerland.
PLoS One. 2025 Jul 24;20(7):e0328833. doi: 10.1371/journal.pone.0328833. eCollection 2025.
Speech analysis offers a non-invasive method for assessing emotional and cognitive states through acoustic correlates, including spectral, prosodic, and voice quality features. Despite growing interest, research remains inconsistent in identifying reliable acoustic markers, providing limited guidance for researchers and practitioners in the field. This review identifies key acoustic correlates for detecting negative emotions, stress, and cognitive load in speech. A systematic search was conducted across four electronic databases: PubMed, PsycInfo, Web of Science, and Scopus. Peer-reviewed articles reporting studies conducted with healthy adult participants were included. Thirty-eight articles were reviewed, encompassing 39 studies, as one article reported on two studies. Among all features, prosodic features were the most investigated and showed the greatest accuracy in detecting negative emotions, stress, and cognitive load. Specifically, anger was associated with elevated fundamental frequency (F0), increased speech volume, and faster speech rate. Stress was associated with increased F0 and intensity, and reduced speech duration. Cognitive load was linked to increased F0 and intensity, although the results for F0 were overall less clear than those for negative emotions and stress. No consistent acoustic patterns were identified for fear or anxiety. The findings support speech analysis as a useful tool for researchers and practitioners aiming to assess negative emotions, stress, and cognitive load in experimental and field studies.
语音分析提供了一种非侵入性方法，可通过声学关联来评估情绪和认知状态，这些关联包括频谱、韵律和语音质量特征。尽管人们对此的兴趣日益浓厚，但在识别可靠的声学标记方面，研究结果仍不一致，这为该领域的研究人员和从业者提供的指导有限。本综述确定了用于检测语音中负面情绪、压力和认知负荷的关键声学关联。我们在四个电子数据库中进行了系统检索：PubMed、PsycInfo、Web of Science和Scopus。纳入了报告对健康成年参与者进行研究的同行评审文章。共审查了38篇文章，涵盖39项研究，因为有一篇文章报告了两项研究。在所有特征中，韵律特征研究最多，在检测负面情绪、压力和认知负荷方面显示出最高的准确性。具体而言，愤怒与基频（F0）升高、音量增加和语速加快有关。压力与F0和强度增加以及言语持续时间缩短有关。认知负荷与F0和强度增加有关，尽管F0的结果总体上不如负面情绪和压力的结果清晰。未发现恐惧或焦虑的一致声学模式。这些发现支持语音分析作为一种有用工具，供研究人员和从业者在实验和实地研究中评估负面情绪、压力和认知负荷。
