情感计算在老年语言学研究中的应用与发展
Affective Computing in Gerontolinguistic Research: Application and Development
DOI: 10.12677/ml.2026.143198, PDF,    科研立项经费支持
作者: 徐子涵：中国海洋大学外国语学院，山东 青岛
关键词: 情感计算；老年语言学研究；应用现状；发展趋势；Affective Computing； Gerontolinguistics Research； Application Status； Development Trends
摘要: 近年来，人工智能技术发展迅速，情感计算(Affective Computing)在老年语言学研究中的应用备受瞩目。在梳理相关文献的基础上，本文首先总结了情感计算应用于老年语言学研究的三种模式：1) 语言情感计算与分析；2) 多模态情感融合；3) 情感交互干预。然后，基于情感计算和老年语言学的交叉研究，总结了情感计算应用于老年语言学研究的前沿进展：1) 情感计算提供的数据多样化；2) 老年语言服务的智能场景。最后，分析了此类研究存在的问题与挑战，并指出了未来的研究趋势。
Abstract: In recent years, artificial intelligence technology has been developing rapidly, and the application of affective computing in the field of gerontolinguistics has attracted much attention. Based on related research literature to date, three modes of applying affective computing to gerontolinguistic research have been first summarized: 1) language emotion computing and analysis; 2) multi-modal emotion fusion; and 3) emotional interaction and intervention. Then, two new development trends are proposed: 1) the diversity of data provided by affective computing; 2) intelligent scenarios for elderly language services. Finally, existing problems and challenges in the application of affective computing in gerontolinguistic are analyzed, and directions for future research are also discussed.
文章引用：徐子涵. 情感计算在老年语言学研究中的应用与发展[J]. 现代语言学, 2026, 14(3): 77-82. https://doi.org/10.12677/ml.2026.143198

参考文献

[1] Picard, R.W. (1997) Affective Computing. MIT Press. [Google Scholar] [CrossRef] 
[2] 姚鸿勋, 邓伟洪, 刘洪海, 等. 情感计算与理解研究发展概述[J]. 中国图象图形学报, 2022, 27(6): 2008-2035.
[3] Smith, E., Storch, E.A., Vahia, I., Wong, S.T.C., Lavretsky, H., Cummings, J.L. and Eyre, H.A. (2021) Affective Computing for Late-Life Mood and Cognitive Disorders. Frontiers in Psychiatry, 12, Article ID: 782183. [Google Scholar] [CrossRef] [PubMed]
[4] 黄立鹤, 叶子. 基于深度学习的老年认知障碍与语言特征研究[J]. 外语与外语教学, 2024(4): 81-90.
[5] 吴峰, 万义文, 仲彧欣. 情感计算技术支持下的老年人在线学习情感分析[J]. 电化教育研究, 2025, 46(6): 41-48.
[6] Thakur, P., Kaur, N., Aggarwal, N., et al. (2025) A Comprehensive Review of Unimodal and Multimodal Emotion Detection: Datasets, Approaches, and Limitations. Expert Systems, 42, e70103. [Google Scholar] [CrossRef] 
[7] Dilana, H.R., Sascha, M., Andreas, D., et al. (2020) The uulmMAC Database—A Multimodal Affective Corpus for Affective Computing in Human-Computer Interaction. Sensors, 20, 2308-2308. [Google Scholar] [CrossRef] [PubMed]
[8] Tsujikawa, S., Tsutsui, H. and Honda, Y. (2023) Development and Application of a Communication Robot to Improve the Emotional State of Elderly Living Alone. Universal Access in the Information Society, 23, 1979-1986. [Google Scholar] [CrossRef] 
[9] Nijmeijer, S.E., van Tol, M.-J., Aleman, A. and Keijzer, M. (2021) Foreign Language Learning as Cognitive Training to Prevent Old Age Disorders? Protocol of a Randomized Controlled Trial of Language Training vs. Musical Training and Social Interaction in Elderly with Subjective Cognitive Decline. Frontiers in Aging Neuroscience, 13, Article ID: 550180. [Google Scholar] [CrossRef] [PubMed]
[10] 王善敏, 刘成广, 陈胜宇, 等. 面向表情、语音和语言的多模态情感识别综述[J]. 中国图象图形学报, 2025, 30(6): 2120-2138.
[11] Tadas, B., Chaitanya, A. and Louis, P.M. (2018) Multimodal Machine Learning: A Survey and Taxonomy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41, 423-443. [Google Scholar] [CrossRef] 
[12] 王一岩, 郑永和. 多模态数据融合: 破解智能教育关键问题的核心驱动力[J]. 现代远程教育研究, 2022, 34(2): 93-102.
[13] 卓翔, 栗洁歆, 王立非. 中国老年语言服务体系建设研究[J]. 社会科学研究, 2022(6): 200-206.
[14] Michail, C., Loukas, I., Dimitris, A., et al. (2023) Neural Architecture Search with Multimodal Fusion Methods for Diagnosing Dementia. ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, 4-10 June 2023, 1-5. [Google Scholar] [CrossRef] 
[15] Ortiz-Perez, D., Pablo, R., Tomás, D., et al. (2023) A Deep Learning-Based Multimodal Architecture to Predict Signs of Dementia. Neurocomputing, 548, Article 126413. [Google Scholar] [CrossRef] 
[16] Agbavor, F. and Liang, H. (2022) Predicting Dementia from Spontaneous Speech Using Large Language Models. PLOS Digital Health, 1, e0000168. [Google Scholar] [CrossRef] [PubMed]
[17] 李瑶, 杨琳. 人工智能应用于心理健康服务的相关问题思考[J]. 医学与哲学, 2022, 43(5): 49-54.