Institute of Psychiatry, Psychology and Neuroscience, Department of Psychosis Studies, King's College London, London SE5 8AF, UK.
Institute of Psychiatry, Psychology and Neuroscience, Department of Psychosis Studies, King's College London, London SE5 8AF, UK.
Schizophr Res. 2023 Sep;259:11-19. doi: 10.1016/j.schres.2023.03.044. Epub 2023 Apr 18.
Remote assessment of acoustic alterations in speech holds promise to increase scalability and validity in research across the psychosis spectrum. A feasible first step in establishing a procedure for online assessments is to assess acoustic alterations in psychometric schizotypy. However, to date, the complex relationship between alterations in speech related to schizotypy and those related to comorbid conditions such as symptoms of depression and anxiety has not been investigated. This study tested whether (1) depression, generalized anxiety and high psychometric schizotypy have similar voice characteristics, (2) which acoustic markers of online collected speech are the strongest predictors of psychometric schizotypy, (3) whether including generalized anxiety and depression symptoms in the model can improve the prediction of schizotypy.
We collected cross-sectional, online-recorded speech data from 441 participants, assessing demographics, symptoms of depression, generalized anxiety and psychometric schizotypy.
Speech samples collected online could predict psychometric schizotypy, depression, and anxiety symptoms with weak to moderate predictive power, and with moderate and good predictive power when basic demographic variables were added to the models. Most influential features of these models largely overlapped. The predictive power of speech marker-based models of schizotypy significantly improved after including symptom scores of depression and generalized anxiety in the models (from R = 0.296 to R = 0. 436).
Acoustic features of online collected speech are predictive of psychometric schizotypy as well as generalized anxiety and depression symptoms. The acoustic characteristics of schizotypy, depression and anxiety symptoms significantly overlap. Speech models that are designed to predict schizotypy or symptoms of the schizophrenia spectrum might therefore benefit from controlling for symptoms of depression and anxiety.
远程评估语音中的声学变化有望提高精神分裂症谱系研究的可扩展性和有效性。建立在线评估程序的可行的第一步是评估精神分裂症特质的声学变化。然而，迄今为止，与精神分裂症特质相关的语音变化与与抑郁和焦虑等共病状况相关的语音变化之间的复杂关系尚未得到研究。本研究检验了以下几点：(1)抑郁、广泛性焦虑和高精神分裂症特质是否具有相似的语音特征；(2)在线采集的语音中有哪些声学标志物是精神分裂症特质的最强预测因子；(3)在模型中包含广泛性焦虑和抑郁症状是否可以提高对精神分裂症特质的预测。
我们从 441 名参与者中收集了横断面、在线记录的语音数据，评估了人口统计学特征、抑郁、广泛性焦虑和精神分裂症特质。
在线采集的语音样本可以预测精神分裂症特质、抑郁和焦虑症状，预测能力为弱到中等，当基本人口统计学变量加入模型后，预测能力为中等和良好。这些模型中最有影响力的特征大部分是重叠的。在将抑郁和广泛性焦虑的症状评分纳入模型后，基于语音标记的精神分裂症模型的预测能力显著提高（从 R=0.296 提高到 R=0.436）。
在线采集的语音的声学特征可以预测精神分裂症特质以及广泛性焦虑和抑郁症状。精神分裂症特质、抑郁和焦虑症状的声学特征有显著重叠。因此，旨在预测精神分裂症特质或精神分裂症谱系症状的语音模型可能受益于控制抑郁和焦虑症状。
