Evaluating Conversational Speech Features for Early Mild Cognitive
Impairment Using Social Robots for Prevention and Treatment

Avika Singhal

doi:10.46609/IJSSER.2026.v11i09.055

Title: Evaluating Conversational Speech Features for Early Mild Cognitive Impairment Using Social Robots for Prevention and Treatment
Authors: Avika Singhal
Journal: International Journal of Social Science and Economic Research (IJSSER) ISSN: 2455-8834 Volume: 11 Issue: 9 Publication Date: September 2026 DOI: 10.46609/IJSSER.2026.v11i09.055
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Avika Singhal Sanskriti The Gurukul
MLA 8 Singhal, Avika. "Evaluating Conversational Speech Features for Early Mild Cognitive Impairment Using Social Robots for Prevention and Treatment." Int. j. of Social Science and Economic Research, vol. 11, no. 9, Sept. 2026, pp. 5348-5363, doi.org/10.46609/IJSSER.2026.v11i09.055. Accessed Sept. 2026. APA 6 Singhal, A. (2026, September). Evaluating Conversational Speech Features for Early Mild Cognitive Impairment Using Social Robots for Prevention and Treatment. Int. j. of Social Science and Economic Research, 11(9), 5348-5363. Retrieved from https://doi.org/10.46609/IJSSER.2026.v11i09.055 Chicago Singhal, Avika. "Evaluating Conversational Speech Features for Early Mild Cognitive Impairment Using Social Robots for Prevention and Treatment." Int. j. of Social Science and Economic Research 11, no. 9 (September 2026), 5348-5363. Accessed September, 2026. https://doi.org/10.46609/IJSSER.2026.v11i09.055.
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ABSTRACT: Mild Cognitive Impairment (MCI) is an important stage between normal cognitive ageing and dementia, making its early identification particularly valuable for timely intervention and longterm care. This paper examines the potential of Artificial Intelligence (AI) in analysing conversational speech as a non-invasive method for identifying subtle indicators of cognitive decline. Speech involves several cognitive processes, including memory, attention, executive functioning, and language. Changes in features such as pauses, speech rate, word-finding difficulties, lexical diversity, and sentence complexity may therefore provide useful information about an individual's cognitive state, and the role of socially assistive robots can be in addressing this cognitive impairment. The role of clinical psychologists is also considered, particularly in interpreting AI-generated information, developing personalised interventions, and making clinical decisions. The analysis suggests that AI-based speech analysis has potential as a complementary approach to conventional cognitive assessments, while social robots can make monitoring more continuous and less dependent on occasional clinical visits. Robot-assisted cognitive training and lifestyle interventions have also shown promising results in the studies examined. However, these technologies currently face limitations, with respect to small study populations, limited long-term validation, language and cultural differences, privacy concerns, patient engagement, and the cost of specialised robotic systems. The paper ultimately argues for a collaborative approach in which AI and social robots assist healthcare professionals rather than replace them. While robots can collect data, provide reminders, deliver structured exercises, and identify changes requiring further attention, clinical psychologists remain essential for interpretation, empathy, diagnosis, and personalised care. The future application of these technologies will therefore depend on combining technological capabilities with human clinical expertise in a responsible and accessible manner.

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