Study: ML using speech can estimate chronological and biological age (www.tcd.ie)

A groundbreaking study published in *Science Advances* reveals that machine learning models analyzing speech can effectively estimate both chronological and biological age. Researchers have developed a "speech clock" that assesses hundreds of acoustic and linguistic features—such as speech rate, pitch, and emotional content—from individuals’ voices. This innovative approach not only predicts a person's age based on their speech patterns but also correlates with various indicators of cognitive health and biological aging, including brain structure and function, as well as dementia risk. This research stands out in the AI and medical fields by suggesting that understanding speech could become a non-invasive, cost-effective method for assessing aging, particularly in resource-limited environments. Unlike traditional biological age assessments that rely on expensive imaging and laboratory tests, the speech clock can be easily recorded and analyzed. Although the current study lacks longitudinal data to confirm predictive capabilities, the implications are significant, highlighting speech as a potential tool for monitoring cognitive decline and aging-related health in diverse populations. If further validated, this could revolutionize how we utilize everyday speech in assessing health and aging.