PDF(Pubmed)
Abstract：
Quantitative acoustic and textual measures derived from speech (\"speech features\") may provide valuable biomarkers for psychiatric disorders, particularly schizophrenia spectrum disorders (SSD). We sought to identify cross-diagnostic latent factors for speech disturbance with relevance for SSD and computational modeling.
Clinical ratings for speech disturbance were generated across 14 items for a cross-diagnostic sample (N = 334), including SSD (n = 90). Speech features were quantified using an automated pipeline for brief recorded samples of free speech. Factor models for the clinical ratings were generated using exploratory factor analysis, then tested with confirmatory factor analysis in the cross-diagnostic and SSD groups. The relationships between factor scores and computational speech features were examined for 202 of the participants.
We found a 3-factor model with a good fit in the cross-diagnostic group and an acceptable fit for the SSD subsample. The model identifies an impaired expressivity factor and 2 interrelated disorganized factors for inefficient and incoherent speech. Incoherent speech was specific to psychosis groups, while inefficient speech and impaired expressivity showed intermediate effects in people with nonpsychotic disorders. Each of the 3 factors had significant and distinct relationships with speech features, which differed for the cross-diagnostic vs SSD groups.
We report a cross-diagnostic 3-factor model for speech disturbance which is supported by good statistical measures, intuitive, applicable to SSD, and relatable to linguistic theories. It provides a valuable framework for understanding speech disturbance and appropriate targets for modeling with quantitative speech features.
Clinical ratings for speech disturbance were generated across 14 items for a cross-diagnostic sample (N = 334), including SSD (n = 90). Speech features were quantified using an automated pipeline for brief recorded samples of free speech. Factor models for the clinical ratings were generated using exploratory factor analysis, then tested with confirmatory factor analysis in the cross-diagnostic and SSD groups. The relationships between factor scores and computational speech features were examined for 202 of the participants.
We found a 3-factor model with a good fit in the cross-diagnostic group and an acceptable fit for the SSD subsample. The model identifies an impaired expressivity factor and 2 interrelated disorganized factors for inefficient and incoherent speech. Incoherent speech was specific to psychosis groups, while inefficient speech and impaired expressivity showed intermediate effects in people with nonpsychotic disorders. Each of the 3 factors had significant and distinct relationships with speech features, which differed for the cross-diagnostic vs SSD groups.
We report a cross-diagnostic 3-factor model for speech disturbance which is supported by good statistical measures, intuitive, applicable to SSD, and relatable to linguistic theories. It provides a valuable framework for understanding speech disturbance and appropriate targets for modeling with quantitative speech features.
摘要：
目的：从语音中获得的定量声学和文本测量（“语音特征”）可能为精神疾病提供有价值的生物标志物，特别是精神分裂症谱系障碍（SSD）。我们试图确定与SSD和计算建模相关的语音干扰的交叉诊断潜在因素。
方法：针对交叉诊断样本（N=334），对14个项目进行了言语障碍的临床评分。包括SSD(n=90)。使用自动管道对语音特征进行量化，以获取简短的语音记录样本。使用探索性因子分析生成临床评级的因子模型，然后在交叉诊断和SSD组中进行验证性因素分析。对202名参与者检查了因子得分与计算语音特征之间的关系。
结果：我们发现了一个3因素模型，该模型在交叉诊断组中具有良好的拟合度，并且对于SSD子样本具有可接受的拟合度。该模型确定了一个受损的表达因子和2个相互关联的无组织因子，以导致低效和不连贯的语音。语无伦次是精神病群体特有的，而低效的言语和受损的表达能力在非精神病患者中显示出中间效应。这3个因素中的每一个都与语音特征有显著和不同的关系，交叉诊断与SSD组不同。
结论：我们报告了一个言语障碍的交叉诊断三因素模型，该模型得到了良好的统计指标的支持，直观，适用于SSD，与语言学理论相关。它为理解语音干扰提供了一个有价值的框架，并为定量语音特征建模提供了适当的目标。
方法：针对交叉诊断样本（N=334），对14个项目进行了言语障碍的临床评分。包括SSD(n=90)。使用自动管道对语音特征进行量化，以获取简短的语音记录样本。使用探索性因子分析生成临床评级的因子模型，然后在交叉诊断和SSD组中进行验证性因素分析。对202名参与者检查了因子得分与计算语音特征之间的关系。
结果：我们发现了一个3因素模型，该模型在交叉诊断组中具有良好的拟合度，并且对于SSD子样本具有可接受的拟合度。该模型确定了一个受损的表达因子和2个相互关联的无组织因子，以导致低效和不连贯的语音。语无伦次是精神病群体特有的，而低效的言语和受损的表达能力在非精神病患者中显示出中间效应。这3个因素中的每一个都与语音特征有显著和不同的关系，交叉诊断与SSD组不同。
结论：我们报告了一个言语障碍的交叉诊断三因素模型，该模型得到了良好的统计指标的支持，直观，适用于SSD，与语言学理论相关。它为理解语音干扰提供了一个有价值的框架，并为定量语音特征建模提供了适当的目标。
