How are We Doing Today? Using Natural Speech Analysis to Assess Older Adults’ Subjective Well-Being

The research presents the development and test of a machine learning (ML) model to assess the subjective well-being of older adults based solely on natural speech. The use of such technologies can have a positive impact on healthcare delivery: the proposed ML model is patient-centric and securely uses user-generated data to provide sustainable value not only in the healthcare context but also to address the global challenge of demographic change, especially with respect to healthy aging. The developed model unobtrusively analyzes the vocal characteristics of older adults by utilizing natural language processing but without using speech recognition capabilities and adhering to the highest privacy standards. It is based on theories of subjective well-being, acoustic phonetics, and prosodic theories. The ML models were trained with voice data from volunteer participants and calibrated through the World Health Organization Quality of Life Questionnaire (WHOQOL), a widely accepted tool for assessing the subjective well-being of human beings. Using WHOQOL scores as a proxy, the developed model provides accurate numerical estimates of individuals’ subjective well-being. Different models were tested and compared. The regression model proves beneficial for detecting unexpected shifts in subjective well-being, whereas the support vector regression model performed best and achieved a mean absolute error of 10.90 with a standard deviation of 2.17. The results enhance the understanding of the subconscious information conveyed through natural speech. This offers multiple applications in healthcare and aging, as well as new ways to collect, analyze, and interpret self-reported user data. Practitioners can use these insights to develop a wealth of innovative products and services to help seniors maintain their independence longer, and physicians can gain much greater insight into changes in their patients’ subjective well-being.