Stars Team, Institut national de recherche en informatique et en automatique, Valbonne, France.
Département de Santé Publique, Centre Hospitalier Universitaire de Nice, Université Côte d'Azur, Nice, France.
J Med Internet Res. 2021 Apr 19;23(4):e24191. doi: 10.2196/24191.
During the COVID-19 pandemic, health professionals have been directly confronted with the suffering of patients and their families. By making them main actors in the management of this health crisis, they have been exposed to various psychosocial risks (stress, trauma, fatigue, etc). Paradoxically, stress-related symptoms are often underreported in this vulnerable population but are potentially detectable through passive monitoring of changes in speech behavior.
This study aims to investigate the use of rapid and remote measures of stress levels in health professionals working during the COVID-19 outbreak. This was done through the analysis of participants' speech behavior during a short phone call conversation and, in particular, via positive, negative, and neutral storytelling tasks.
Speech samples from 89 health care professionals were collected over the phone during positive, negative, and neutral storytelling tasks; various voice features were extracted and compared with classical stress measures via standard questionnaires. Additionally, a regression analysis was performed.
Certain speech characteristics correlated with stress levels in both genders; mainly, spectral (ie, formant) features, such as the mel-frequency cepstral coefficient, and prosodic characteristics, such as the fundamental frequency, appeared to be sensitive to stress. Overall, for both male and female participants, using vocal features from the positive tasks for regression yielded the most accurate prediction results of stress scores (mean absolute error 5.31).
Automatic speech analysis could help with early detection of subtle signs of stress in vulnerable populations over the phone. By combining the use of this technology with timely intervention strategies, it could contribute to the prevention of burnout and the development of comorbidities, such as depression or anxiety.
在 COVID-19 大流行期间，卫生专业人员直接面对患者及其家属的痛苦。通过使他们成为管理这场卫生危机的主要参与者，他们接触到了各种心理社会风险（压力、创伤、疲劳等）。矛盾的是，与压力相关的症状在这个弱势群体中经常被低估，但通过被动监测言语行为的变化，这些症状可能是可检测的。
本研究旨在调查在 COVID-19 爆发期间使用快速远程测量卫生专业人员压力水平的方法。这是通过分析参与者在简短电话交谈期间的言语行为来实现的，特别是通过积极、消极和中性讲故事任务。
通过电话收集了 89 名医护人员在积极、消极和中性讲故事任务期间的言语样本；提取了各种语音特征，并通过标准问卷与经典压力测量进行了比较。此外，还进行了回归分析。
某些言语特征与两性的压力水平相关；主要是频谱（即共振峰）特征，如梅尔频率倒谱系数，以及韵律特征，如基频，似乎对压力敏感。总体而言，对于男性和女性参与者，使用正任务中的声纹特征进行回归可得出压力评分的最准确预测结果（平均绝对误差 5.31）。
自动语音分析可以帮助通过电话在弱势群体中早期检测到压力的微妙迹象。通过将这项技术与及时的干预策略相结合，它可以有助于预防倦怠和共病的发展，如抑郁或焦虑。
