发表机构
University of Pennsylvania; Leonard Davis Institute of Health Economics; SeniorsTogether, Inc.; Texas A&M University; Johns Hopkins School of Medicine（宾夕法尼亚大学; 伦纳德·戴维斯健康经济学研究所; SeniorsTogether公司; 德克萨斯农工大学; 约翰斯·霍普金斯大学医学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对老年人孤独感检测方法有限的问题，采用结合语言与声学特征的多模态框架，发现多模态模型表现优于单模态模型，证实语音分析可辅助情感孤独感的早期评估。
AI 中文摘要
孤独感是老年人中一项关键的公共卫生问题，与抑郁、认知衰退及死亡的更高风险相关。目前针对孤独感的可扩展、客观检测方法仍然有限，尤其在自然对话场景中。我们通过半结构化电话访谈分析了310名老年人的孤独感语音与语言标记，以了解他们如何处理孤独感受，以及在不同孤独感水平下的语言差异。我们的多模态框架结合了语言特征（心理语言学词典、n-gram、主题模型）与声学特征（音高、语调、响度），以检验与自我报告孤独感评分的关联。预定义方法与数据驱动方法均捕捉到了言语内容与语音表达中的模式：更高的孤独感与否定词（r=0.11）、负面语调（r=0.12）及冲突相关语言相关；更低的孤独感与社会参照（r=-0.18）、动机驱动（r=-0.11）及语音中的情感丰富度（r=-0.12）相关。我们还发现，多模态模型（r=0.298）的表现优于仅文本模型与仅音频模型。研究结果表明，孤独感通过语言与声学线索共同体现，支持语音分析在心理评估中的潜力，当与现有评估工具结合使用时，可作为情感孤独感的早期指标，而非独立诊断工具。
英文摘要
Loneliness is a critical public health issue among older adults, linked to higher risks of depression, cognitive decline, and mortality. Scalable, objective methods for its detection remain limited, particularly in natural conversational contexts. We analyzed speech and language markers of loneliness in 310 older adults using semi-structured telephone interviews to help understand how they process feeling lonely and how their language differs at different levels of feeling loneliness. Our multimodal framework combined linguistic features (psycholinguistic dictionaries, n-grams, and topic models) with acoustic features (pitch, tone, loudness) to examine associations with self-reported loneliness scores. Both predefined and data-driven methods captured patterns in verbal content and vocal delivery. Higher loneliness was associated with negations(r = 0.11), negative tone(r = 0.12), and conflict-related language. Lower loneliness was linked to social references(r = -0.18), motivational drives(r = -0.11), and emotional richness in speech(r = -0.12). We also found that the multimodal model (r = 0.298) outperforms the text-only and audio-only models. Findings suggest that loneliness manifests through both linguistic and acoustic cues, supporting the potential of speech-based analysis in psychological assessments and as an early indicator of emotional loneliness when used alongside existing assessments, rather than as standalone diagnostic tools.
