A new artificial intelligence (AI) tool will be developed to detect early signs of depression in elderly individuals by analysing their voices.
As part of a three-year research study and a pilot programme named ‘SoundKeepers’, seven partners from the healthcare and social sectors in Singapore will come together to create an AI tool using voice biomarkers to detect early signs of depression in seniors.
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This AI tool, once developed, will collect and analyse voice recordings with the consent of senior citizens to assess their mental health.
The seven partners include – Nanyang Technological University, Singapore’s (NTU Singapore) Lee Kong Chian School of Medicine and College of Computing and Data Science (CCDS), National Healthcare Group Polyclinics and Institute of Mental Health, Fei Yue Community Services and Club HEAL, and Lien Foundation.
“We need new ways to listen to our seniors. While they may not express their worries through words, we can now try to hear it through their voices,” said Lee Poh Wah, CEO, Lien Foundation.
The project – SoundKeepers – involves over 600 seniors and aims to help seniors aged 55 years old and above suffering from subsyndromal depression (SSD).
What’s SSD
SSD is a stage where depressive symptoms start to emerge but are not yet severe enough to warrant a diagnosis. Among seniors, it is a largely unaddressed health risk, the researchers explained.
Individuals with SSD are five times more likely to develop depression within a year and have a 12 times higher risk of dementia.
“Currently SSD is not actively diagnosed or treated. However, with the focus on early detection and treatment emphasised by both HealthierSG and the National Mental Health and WellBeing Strategy, this project becomes extremely relevant as it can facilitate the early detection and diagnosis of SSD with a tool that can be easily used in the community setting,” said Dr Mythily Subramaniam, Assistant Chairman, Medical Board (Research), IMH and CoPrincipal Investigator of SoundKeepers.
How the programme will work
There will be two components to the programme structure.
In the first component, voice recordings of the participants from casual conversations or passage reading will be collected with consent and identified as a representative sample for the AI model.
The voice samples will be anonymised and stored in a central storage terminal.
The AI will then examine these sample recordings for their acoustic properties, including pitch, volume, timbre, rhythm, shimmer, jitter, and harmonics-to-noise ratio.
“When we use our voice, we are activating and coordinating more than a hundred different muscles and neurobiological processes. A change in speech acoustic features can reveal abnormalities in these neurobiological processes,” explained Dr Lee Eng Sing, co-principal project investigator.
A second component of the project is a 24-week community intervention program led by the Institute of Mental Health (IMH).
This programme will include module-based psychoeducation, community activities (such as exercise and recreational activities), and befriending initiatives.
It will be delivered with support from social service agencies, including Fei Yue Community Services and Club HEAL.
Additionally, a randomised controlled trial, led by the NHG Polyclinics, will recruit 300 participants showing signs of early depression.
The study aims to compare their levels of loneliness, anxiety, well-being, and depression literacy before and after the intervention program.
These results will also be compared to a control group.
This comprehensive project has been awarded approximately $4.2 million in funding from the Lien Foundation.