Predictors of Loneliness in Older Adults Using Multimodal Analysis of Speech and Language

2026-09-02

AI Summary

研究通过分析310名老年人的电话访谈，结合语言和声学特征预测孤独感，发现多模态模型在评估孤独感方面优于单一文本或音频模型。

Abstract

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.

Problem

• Loneliness
• Older Adults
• Speech and Language
• Natural Conversational Contexts
• Psychological Assessments

Innovation

• multimodal analysis
• linguistic features
• acoustic features
• loneliness detection