Centre for Mental Health, Swinburne University of Technology, Melbourne, Australia; Department of Psychiatry, St. Vincent's Hospital, Melbourne, Australia.
Centre for Mental Health, Swinburne University of Technology, Melbourne, Australia.
Schizophr Res. 2021 Dec;238:91-98. doi: 10.1016/j.schres.2021.10.003. Epub 2021 Oct 11.
Speech disturbances are a recognised aspect of schizophrenia that may have potential utility as a diagnostic indicator. Recent advances in quantitative speech assessment methods have led to more reproducible and precise metrics making this possible. The current study sought firstly to characterise the speech profile of schizophrenia patients using quantitative speech measures, then examine the diagnostic utility of these measures and explore their relationship to symptoms.
Speech recordings from 43 schizophrenia/schizoaffective disorder (SZ) patients and 46 healthy controls (HC) were obtained and transcribed. Cognitive and symptom measures were also administered.
Compared to HCs, SZ patients had higher incidences of aberrance across five types of quantitative speech variables: utterances, single words, time/speaking rate, turns and formulation errors, but not pauses. Based on two machine learning algorithms, 21 speech variables across the same five speech variable types (again not including pauses) were identified as significant classifiers for a schizophrenia diagnosis with 90-100% specificity and 80-90% sensitivity for both models. Selective relationships were also observed between these speech variables and only positive, disorganisation, excitement and formal thought disorder symptoms.
The findings support pervasive speech impairments in schizophrenia patients relative to HCs, and the potential diagnostic utility of these speech disturbances. Continued work is needed to build the evidence base for quantitative speech assessment as a future objective diagnostic tool for schizophrenia. It holds the promise of improved diagnostic accuracy leading to increased treatment efficacy and better patient outcomes.
言语障碍是精神分裂症的一个公认特征，可能具有作为诊断指标的潜在效用。定量言语评估方法的最新进展使得这成为可能，产生了更具重现性和更精确的指标。本研究首先旨在使用定量言语测量来描述精神分裂症患者的言语特征，然后检查这些测量的诊断效用，并探讨它们与症状的关系。
从 43 名精神分裂症/分裂情感障碍（SZ）患者和 46 名健康对照（HC）中获得并转录了言语录音。还进行了认知和症状测量。
与 HC 相比，SZ 患者在五种类型的定量言语变量中出现异常的发生率更高：话语、单字、时间/说话率、轮次和构词错误，但不包括停顿。基于两种机器学习算法，21 个言语变量在相同的五种言语变量类型（同样不包括停顿）被确定为用于精神分裂症诊断的显著分类器，两个模型的特异性均为 90-100%，敏感性为 80-90%。还观察到这些言语变量与阳性、紊乱、兴奋和形式思维障碍症状之间存在选择性关系。
研究结果支持与 HC 相比，SZ 患者存在普遍的言语障碍，这些言语障碍具有潜在的诊断效用。需要进一步的工作来建立定量言语评估作为精神分裂症未来客观诊断工具的证据基础。它有望提高诊断准确性，从而提高治疗效果并改善患者结局。
