A new AI tool that analyzes speech could give users a glimpse of how they are aging and whether they’re at risk of dementia, a new study suggests.
The tool analyzes hundreds of speech and language patterns to estimate the user’s chronological age from the way they talk. In a study involving nearly 3,000 Spanish-speaking adults, those whose estimated age was higher than their actual age were more likely to have cognitive problems, dementia and signs of accelerated biological aging, researchers reported Wednesday (Sept. 30) in the journal Science Advances. They were also more likely to have less favorable social and economic circumstances than other study participants.
This voice-analyzing tool is similar to other “aging clocks,” which use various biological markers to predict people’s risk of age-related diseases.
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“Existing aging clocks are very powerful, but many of them rely on brain scans or blood tests that are expensive, invasive or difficult to repeat regularly,” said study co-author Adolfo García, director of the Cognitive Neuroscience Center at the University of San Andrés in Argentina. “All of these limitations can be overcome with speech,” he argued.
However, the approach still needs to be tested over time and in other languages before it can be used to assess individual patients’ risk of dementia, an expert told Live Science.
How the speech clock works
Researchers used data from the ReD-Lat consortium, a large dementia research project in Latin America. The analysis included over 2,900 Spanish-speaking adults from Argentina, Chile, Colombia, Mexico and Peru; their ages ranged from 18 to 88. Of them, about 1,500 were cognitively healthy; the others had mild cognitive impairment (usually marked by mild memory problems and trouble thinking), Alzheimer’s disease or frontotemporal dementia.
Each person completed seven tasks, such as describing an animated video; naming as many words, vegetables or animals as possible in 60 seconds; and retelling a short story immediately after hearing it and then again 20 to 30 minutes later. The scientists took recordings, transcribed them, and then identified more than 700 distinct features of the recordings, including pauses, speaking speed, pitch, vocabulary and emotional expression.
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They trained a machine-learning model using those features and the participants’ chronological ages, teaching it to estimate age from the way a person spoke. The model generated a predicted age for each participant, and researchers compared it with each participant’s actual age to calculate what they called a “speech-age gap.”
Some people’s speech appeared older than expected for their age, the AI tool determined. Cognitively healthy people had the smallest gaps between their true age and the age the AI assigned them, while people with mild cognitive impairment or dementia had larger gaps. The largest gaps were seen in people with language-dominant frontotemporal dementia, which primarily affects communication.
The human voice is incredibly sensitive to our overall well-being, which means a person’s speech could sound ‘older’ simply because they are severely depressed, exhausted, or navigating serious life stress.
Manisha Parulekar, co-director of the Center for Memory Loss and Brain Health at Hackensack University Medical Center in New Jersey
People with older-sounding speech also performed worse on various tests of memory, language and attention, and they had more difficulty with everyday function. Blood tests showed a similar pattern: Larger gaps were associated with greater age acceleration on three epigenetic clocks, which estimate a person’s biological age by looking at chemical tags on top of their DNA. These epigenetic markers affect which genes can be switched on and to what degree.
Among people with Alzheimer’s disease, larger gaps were also linked to higher levels of p-tau217, a protein associated with the disease.
Larger gaps were also correlated with various risk factors for dementia, such as having financial difficulties, food insecurity, limited healthcare, difficult childhoods or less education. The pattern was similar across all five countries, despite their social and cultural differences, García noted.
What the speech clock cannot tell us yet
The approach is “a huge leap toward making brain health assessments accessible to everyone,” said Dr. Manisha Parulekar, co-director of the Center for Memory Loss and Brain Health at Hackensack University Medical Center in New Jersey, who was not involved in the study.
But the speech clock is not a crystal ball. “The human voice is incredibly sensitive to our overall well-being, which means a person’s speech could sound ‘older’ simply because they are severely depressed, exhausted, or navigating serious life stress,” Parulekar told Live Science in an email. “As a clinician, I wouldn’t use this tool to definitively diagnose someone with dementia in isolation.”
Instead, the speech measure could serve as an additional tool that prompts doctors to take a closer look at a patient’s cognitive health, she suggested.
In the study, many participants were assessed at a single point in time rather than monitored over a longer period. The researchers therefore could not determine whether an older-sounding voice can predict whether a patient will soon experience cognitive decline, or whether certain voice changes occur as dementia progresses. Plus, the model was trained and tested only in Spanish-speaking participants from five Latin American countries.
“We cannot just take the Spanish model and plug English into it,” Parulekar said. The language-specific features would need to be adapted and tested in other populations, she explained.
The researchers now plan to test the approach with other languages and follow people over time to see whether changes in speech can signal future cognitive decline.
“I truly believe that there is a very, very strong signal in speech to anticipate dementia and conversion in the future,” García said, “but we need many more diverse studies.”
This article is for informational purposes only and is not meant to offer medical advice.
Hernandez, H., Pedraza, L. K., Santamaria-Garcia, H., Moguilner, S., Legaz, A., Prado, P., Cuadros, J., Amoruso, L., Gonzalez, L., Dellavale, D., Espinoza–Puelles, J. P., Espinosa, J. P., Jarne, C., Mattiussi, F., Caccia, M., Welford, A. S., Pelella, N., Inchauspe, J., Ferrante, F. J., … Ibanez, A. (2026). Speech clocks decode dementia phenotypes, social exposome, and biological aging. Science Advances, 12(40), eaef9864. https://doi.org/10.1126/sciadv.aef9864
