Applications and Challenges of Speech Emotion Recognition in Geriatric Healthcare: A Scoping Review.
Introduction: Speech emotion recognition (SER), with its non-invasiveness and ease of deployment, offers an innovative solution for geriatric health monitoring. This scoping review aimed to map the evidence on speech emotion recognition, focusing on its application scenarios, core technologies, and...
| Publicado en: | Inquiry (00469580) Vol. 63; pp. 1 - 21 |
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| Autores principales: | , , , , , , , , |
| Formato: | research systematic review tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
8/12/2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=196160127&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 196160127 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00469580 INQ jtl: Inquiry (00469580) issn: 00469580 maglogo: Y pubinfo: dt: 8/12/2026 vid: 63 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 196160127 196160127 196160127 10.1177/00469580261476281 196160127 ppf: 1 ppct: 20 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Applications and Challenges of Speech Emotion Recognition in Geriatric Healthcare: A Scoping Review. aug: au: Wang, Wenxia Yao, Hui Si, Xia Qin, Xiaojing Li, Zian Han, Shuyu Zhang, Muhan Wang, Yapeng Wang, Zhiwen affil: School of Nursing, Peking University, Beijing, China sug: subj: Voice Recognition Systems Emotions Algorithms Health Services for Older Persons Human Scoping Review PubMed Gerontologic Care Long Term Care Mental Health Evaluation Dementia Robotics Speech Acoustics Machine Learning Deep Learning Thematic Analysis Checklists Descriptive Statistics Data Analysis Software Funding Source ab: Introduction: Speech emotion recognition (SER), with its non-invasiveness and ease of deployment, offers an innovative solution for geriatric health monitoring. This scoping review aimed to map the evidence on speech emotion recognition, focusing on its application scenarios, core technologies, and key challenges in geriatric healthcare. Methods: Following Arksey and O'Malley's framework, a comprehensive search of PubMed, Web of Science Core Collection, IEEE Xplore databases were conducted to find studies from the inception to December 2024. Relevant data were extracted from eligible studies. Characteristics of included studies were tabulated, and a narrative synthesis was conducted to address predefined research questions. Results: Thirteen studies were included. Geriatric SER was applied or proposed in home and community monitoring, long-term care, mental health assessment, dementia-related contexts, assistive systems, and methodological or benchmark development. Data sources and emotion targets were heterogeneous, including real-world older-adult speech, challenge datasets, acted or media-derived data, robot-mediated interaction data, and clinical or cognition-related datasets. Technical approaches ranged from acoustic feature extraction and conventional machine learning to deep learning, transfer learning, voice-text modelling, and multimodal fusion. Reported performance varied substantially across studies and was difficult to compare because of differences in datasets, emotion categories, modalities, validation strategies, and performance metrics. Common limitations included small or single-source datasets, inconsistent labelling procedures, limited reporting of label reliability, insufficient external validation, and incomplete reproducibility. Conclusion: Geriatric SER is an emerging but methodologically heterogeneous field with potential value for non-invasive emotional and mental health monitoring. Future research should prioritize standardized elderly-specific datasets, transparent annotation and reporting protocols, participant-level and external validation, real-world deployment studies, and explainable AI approaches that connect model outputs with clinically meaningful indicators. pubtype: Academic Journal doctype: research systematic review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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