Kaczmarek-Majer Katarzyna, Dominiak Monika, Antosik Anna Z, Hryniewicz Olgierd, Kamińska Olga, Opara Karol, Owsiński Jan, Radziszewska Weronika, Sochacka Małgorzata, Święcicki Łukasz
Department of Stochastic Methods, Systems Research Institute Polish Academy of Sciences, Warsaw, Poland.
Department of Pharmacology and Physiology of the Nervous System, Institute of Psychiatry and Neurology, Warsaw, Poland.
Acta Psychiatr Scand. 2025 Mar;151(3):358-374. doi: 10.1111/acps.13735. Epub 2024 Aug 8.
Voice features could be a sensitive marker of affective state in bipolar disorder (BD). Smartphone apps offer an excellent opportunity to collect voice data in the natural setting and become a useful tool in phase prediction in BD.
We investigate the relations between the symptoms of BD, evaluated by psychiatrists, and patients' voice characteristics. A smartphone app extracted acoustic parameters from the daily phone calls of n = 51 patients. We show how the prosodic, spectral, and voice quality features correlate with clinically assessed affective states and explore their usefulness in predicting the BD phase.
A smartphone app (BDmon) was developed to collect the voice signal and extract its physical features. BD patients used the application on average for 208 days. Psychiatrists assessed the severity of BD symptoms using the Hamilton depression rating scale -17 and the Young Mania rating scale. We analyze the relations between acoustic features of speech and patients' mental states using linear generalized mixed-effect models.
The prosodic, spectral, and voice quality parameters, are valid markers in assessing the severity of manic and depressive symptoms. The accuracy of the predictive generalized mixed-effect model is 70.9%-71.4%. Significant differences in the effect sizes and directions are observed between female and male subgroups. The greater the severity of mania in males, the louder (β = 1.6) and higher the tone of voice (β = 0.71), more clearly (β = 1.35), and more sharply they speak (β = 0.95), and their conversations are longer (β = 1.64). For females, the observations are either exactly the opposite-the greater the severity of mania, the quieter (β = -0.27) and lower the tone of voice (β = -0.21) and less clearly (β = -0.25) they speak - or no correlations are found (length of speech). On the other hand, the greater the severity of bipolar depression in males, the quieter (β = -1.07) and less clearly they speak (β = -1.00). In females, no distinct correlations between the severity of depressive symptoms and the change in voice parameters are found.
Speech analysis provides physiological markers of affective symptoms in BD and acoustic features extracted from speech are effective in predicting BD phases. This could personalize monitoring and care for BD patients, helping to decide whether a specialist should be consulted.
语音特征可能是双相情感障碍（BD）情感状态的敏感标志物。智能手机应用程序为在自然环境中收集语音数据提供了绝佳机会，并成为BD阶段预测的有用工具。
我们研究了精神科医生评估的BD症状与患者语音特征之间的关系。一款智能手机应用程序从n = 51名患者的日常电话中提取声学参数。我们展示了韵律、频谱和语音质量特征如何与临床评估的情感状态相关，并探讨它们在预测BD阶段中的有用性。
开发了一款智能手机应用程序（BDmon）来收集语音信号并提取其物理特征。BD患者平均使用该应用程序208天。精神科医生使用汉密尔顿抑郁量表-17和杨氏躁狂量表评估BD症状的严重程度。我们使用线性广义混合效应模型分析语音的声学特征与患者心理状态之间的关系。
韵律、频谱和语音质量参数是评估躁狂和抑郁症状严重程度的有效标志物。预测性广义混合效应模型的准确率为70.9%-71.4%。在男性和女性亚组之间观察到效应大小和方向的显著差异。男性躁狂症状越严重，声音越大（β = 1.6）、音调越高（β = 0.71）、说话越清晰（β = 1.35）、越尖锐（β = 0.95），并且他们的对话越长（β = 1.64）。对于女性，观察结果要么完全相反——躁狂症状越严重，说话越安静（β = -0.27）、音调越低（β = -0.21）、越不清晰（β = -0.25）——要么未发现相关性（语音长度）。另一方面，男性双相抑郁症状越严重，说话越安静（β = -1.07）、越不清晰（β = -1.00）。在女性中，未发现抑郁症状严重程度与语音参数变化之间有明显相关性。
语音分析提供了BD情感症状的生理标志物，从语音中提取的声学特征在预测BD阶段方面是有效的。这可以为BD患者的监测和护理提供个性化服务，有助于决定是否应咨询专科医生。
