Predictors of Loneliness in Older Adults Using Multimodal Analysis of Speech and Language
- URL: http://arxiv.org/abs/2609.02606v1
- Date: Wed, 02 Sep 2026 13:48:14 GMT
- Title: Predictors of Loneliness in Older Adults Using Multimodal Analysis of Speech and Language
- Abstract summary: Loneliness is a critical public health issue among older adults, linked to higher risks of depression, cognitive decline, and mortality.<n>We analyzed speech and language markers of loneliness in 310 older adults using semi-structured telephone interviews.
- Score: 7.541892474431598
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
Related papers
- The False Resonance: A Critical Examination of Emotion Embedding Similarity for Speech Generation Evaluation [66.28943752785906]
Objective metrics for emotional expressiveness are vital for speech generation, particularly in expressive synthesis and voice conversion.<n>We compute cosine similarity of embeddings from encoders like emotion2vec, assuming they capture affective cues despite linguistic and speaker variations.<n>Despite high classification accuracy, these latent spaces are unsuitable for zero-shot similarity evaluation.
arXiv Detail & Related papers (2026-04-29T06:59:48Z) - SocialOmni: Benchmarking Audio-Visual Social Interactivity in Omni Models [86.19617358080016]
Social Omni is a benchmark that operationalizes the evaluation of conversational interactivity across three core dimensions.<n>Social Omni features 2,000 perception samples and a quality-controlled diagnostic set of 209 interaction-generation instances.<n>Our analysis reveals a pronounced decoupling between a model's perceptual accuracy and its ability to generate contextually appropriate interruptions.
arXiv Detail & Related papers (2026-03-17T17:58:44Z) - A Unified Spoken Language Model with Injected Emotional-Attribution Thinking for Human-like Interaction [50.05919688888947]
This paper presents a unified spoken language model for emotional intelligence, enhanced by a novel data construction strategy termed Injected Emotional-Attribution Thinking (IEAT)<n>IEAT incorporates user emotional states and their underlying causes into the model's internal reasoning process, enabling emotion-aware reasoning to be internalized rather than treated as explicit supervision.<n> Experiments on the Human-like Spoken Dialogue Systems Challenge (HumDial) Emotional Intelligence benchmark demonstrate that the proposed approach achieves top-ranked performance across emotional trajectory modeling, emotional reasoning, and empathetic response generation.
arXiv Detail & Related papers (2026-01-08T14:07:30Z) - EchoMind: An Interrelated Multi-level Benchmark for Evaluating Empathetic Speech Language Models [47.41816926003011]
Speech Language Models (SLMs) have made significant progress in spoken language understanding.<n>It remains unclear whether SLMs can fully perceive non lexical vocal cues alongside spoken words, and respond with empathy that aligns with both emotional and contextual factors.<n>We present EchoMind, the first interrelated, multi-level benchmark that simulates the cognitive process of empathetic dialogue.
arXiv Detail & Related papers (2025-10-26T17:15:56Z) - The Expressions of Depression and Anxiety in Chinese Psycho-counseling: Usage of First-person Singular Pronoun and Negative Emotional Words [25.87897084393168]
This study explores the relationship between linguistic expressions and psychological states of depression and anxiety within Chinese psycho-counseling interactions.<n>It employed a general linear mixed-effect model to assess linguistic patterns quantified by the Linguistic Inquiry and Word Count software.<n>Results indicate a significant positive correlation between the frequency of negative emotional words and the severity of both depressive and anxious states among clients.
arXiv Detail & Related papers (2025-07-18T11:53:15Z) - Language-Agnostic Analysis of Speech Depression Detection [2.5764071253486636]
This work analyzes automatic speech-based depression detection across two languages, English and Malayalam.
A CNN model is trained to identify acoustic features associated with depression in speech, focusing on both languages.
Our findings and collected data could contribute to the development of language-agnostic speech-based depression detection systems.
arXiv Detail & Related papers (2024-09-23T07:35:56Z) - LonXplain: Lonesomeness as a Consequence of Mental Disturbance in Reddit
Posts [0.41998444721319217]
Social media is a potential source of information that infers latent mental states through Natural Language Processing (NLP)
Existing literature on psychological theories points to loneliness as the major consequence of interpersonal risk factors.
We formulate lonesomeness detection in social media posts as an explainable binary classification problem.
arXiv Detail & Related papers (2023-05-30T04:21:24Z) - Many Ways to be Lonely: Fine-grained Characterization of Loneliness and
its Potential Changes in COVID-19 [3.2443914909457585]
Loneliness has been associated with negative outcomes for physical and mental health.
To examine how different forms of loneliness and coping strategies manifest in loneliness self-disclosure, we built a dataset, FIG-Loneliness.
We used Reddit posts in two young adult-focused forums and two loneliness related forums consisting of a diverse age group.
arXiv Detail & Related papers (2022-01-19T05:22:55Z) - Perception Point: Identifying Critical Learning Periods in Speech for
Bilingual Networks [58.24134321728942]
We compare and identify cognitive aspects on deep neural-based visual lip-reading models.
We observe a strong correlation between these theories in cognitive psychology and our unique modeling.
arXiv Detail & Related papers (2021-10-13T05:30:50Z) - Model-based analysis of brain activity reveals the hierarchy of language
in 305 subjects [82.81964713263483]
A popular approach to decompose the neural bases of language consists in correlating, across individuals, the brain responses to different stimuli.
Here, we show that a model-based approach can reach equivalent results within subjects exposed to natural stimuli.
arXiv Detail & Related papers (2021-10-12T15:30:21Z) - SOLO: A Corpus of Tweets for Examining the State of Being Alone [27.48937439806902]
We present SOLO (State of Being Alone), a corpus of over 4 million tweets collected with query terms'solitude', 'lonely', and 'loneliness'
We show that the term'solitude' tends to co-occur with more positive, high-dominance words (e.g., enjoy, bliss) while the terms 'lonely' and 'loneliness' frequently co-occur with negative, low-dominance words.
arXiv Detail & Related papers (2020-06-04T18:46:02Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.