arXiv — NLP / Computation & Language · · 3 min read

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

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Computer Science > Computation and Language

arXiv:2609.02606 (cs)
[Submitted on 2 Sep 2026]

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

View a PDF of the paper titled Predictors of Loneliness in Older Adults Using Multimodal Analysis of Speech and Language, by Vinmay Khandode and 8 other authors
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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.
Subjects: Computation and Language (cs.CL)
Cite as: arXiv:2609.02606 [cs.CL]
  (or arXiv:2609.02606v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2609.02606
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Neil Sehgal [view email]
[v1] Wed, 2 Sep 2026 13:48:14 UTC (913 KB)
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