Depression has been associated with heightened first-person singular pronoun use (I-usage; e.g., "I," "my") and negative emotion words. However, past research has relied on nonclinical samples and nonspecific depression measures, raising the question of whether these features are unique to depression vis-à-vis frequently co-occurring conditions, especially anxiety. Using structured questions about recent life changes or difficulties, we interviewed a sample of individuals with varying levels of depression and anxiety (N = 486), including individuals in a major depressive episode (n = 228) and/or diagnosed with generalized anxiety disorder (n = 273). Interviews were transcribed to provide a natural language sample. Analyses isolated language features associated with gold standard, clinician-rated measures of depression and anxiety. Many language features associated with depression were in fact shared between depression and anxiety. Language markers with relative specificity to depression included I-usage, sadness, and decreased positive emotion, while negations (e.g., "not," "no"), negative emotion, and several emotional language markers (e.g., anxiety, stress, depression) were relatively specific to anxiety. Several of these results were replicated using a self-report measure designed to disentangle components of depression and anxiety. We next built machine learning models to detect severity of common and specific depression and anxiety using only interview language. Individuals' speech characteristics during this brief interview predicted their depression and anxiety severity, beyond other clinical and demographic variables. Depression and anxiety have partially distinct patterns of expression in spoken language. Monitoring of depression and anxiety severity via language can augment traditional assessment modalities and aid in early detection. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

中文翻译：

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抑郁和焦虑有不同且重叠的语言模式：临床访谈的结果。

抑郁症与第一人称单数代词的使用（I 用法；例如“我”、“我的”）和负面情绪词的使用有关。然而，过去的研究依赖于非临床样本和非特异性抑郁症测量方法，这就提出了一个问题：这些特征是否是抑郁症相对于经常同时出现的病症（尤其是焦虑症）所独有的。使用有关近期生活变化或困难的结构化问题，我们采访了具有不同程度抑郁和焦虑的个体样本（N = 486），其中包括处于重度抑郁发作的个体（n = 228）和/或被诊断患有广泛性焦虑症（ n = 273）。采访内容被转录以提供自然语言样本。分析与黄金标准、临床医生评定的抑郁和焦虑测量相关的孤立语言特征。事实上，许多与抑郁症相关的语言特征在抑郁症和焦虑症之间是共有的。对抑郁症具有相对特异性的语言标记包括 I-使用、悲伤和积极情绪减少，而否定（例如“不”、“不”）、消极情绪和几种情绪语言标记（例如焦虑、压力、抑郁）与焦虑相对特定。其中一些结果通过旨在理清抑郁和焦虑成分的自我报告测量得到了重复。接下来，我们构建了机器学习模型，仅使用访谈语言来检测常见和特定抑郁和焦虑的严重程度。在这次简短的采访中，个人的言语特征预测了他们抑郁和焦虑的严重程度，超越了其他临床和人口统计变量。抑郁和焦虑在口语中具有部分不同的表达模式。通过语言监测抑郁和焦虑的严重程度可以增强传统的评估方式并有助于早期发现。（PsycInfo 数据库记录 (c) 2023 APA，保留所有权利）。