Automated language analysis to detect Alzheimer’s and frontotemporal dementia in Latino and English-speaking adults
Official title: An automated machine learning approach to language changes in Alzheimer’s disease and frontotemporal dementia across Latino and English-speaking populations
Funded at University of California, San Francisco · NIH-11553465 · Data updated 2026-11-12
A low-cost, computer-based language and speech analysis tool will help identify Alzheimer’s disease and frontotemporal dementia in Latino and English-speaking adults.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of California, San Francisco NIH-funded |
| Lab location | 1 site (San Francisco, United States) |
| Project start | August 15, 2023 |
| Project end | June 30, 2028 |
| Funding year | 2026 |
| Award (this year) | $445,998 |
| NIH project number | 3R01AG075775-04S1 |
| Project ID | NIH-11553465 on NIH RePORTER |
Who could benefit from this research
✓Could help
Adults (ages typically affected by AD/FTD) who are Spanish-speaking or English-speaking, including diverse socio-biological backgrounds and multilingual individuals.
✕Less likely to help
Some patients may not benefit if they do not have language-related markers detectable by speech analyses or do not access the study sites.
What this research studies
This project uses easy-to-collect speech samples to build a machine learning framework that can distinguish Alzheimer’s disease and frontotemporal dementia across diverse language and cultural backgrounds. It focuses on 2,740 participants including Spanish speakers from Latin America and English speakers in the US, and will examine how factors like bilingualism and brain profiles affect markers. The study aims to create robust, culturally valid ASLA measures that work across languages and socio-biological contexts without relying on expensive standard tests. By comparing new speech-based markers with traditional measures, it seeks to improve early detection and monitoring for Latino communities. The approach combines acoustic and linguistic data to generate scalable, low-cost diagnostics that could be used in real-world clinical or community settings.
Why it matters
Potential benefit: If successful, patients could receive earlier, more accessible detection and monitoring for AD and FTD through simple speech-based assessments.
How similar studies have gone: Previous studies show promise for speech-based markers, but this project emphasizes cross-language validity and large-scale diversity, which is novel.
Where this research is happening
-
University of California, San Francisco
San Francisco, United States Active
Researchers
- Principal investigator: Gorno Tempini, Maria Luisa — University of California, San Francisco
- Study coordinator: Gorno Tempini, Maria Luisa
Related research
Other NIH-funded projects on the same topics.
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Automated language analysis for Alzheimer’s disease and frontotemporal dementia in Latino and English speakers
A low-cost, automated speech and language analysis tool is being developed to help identify Alzheimer’s disease or frontotemporal dementia in Latino and English-speaking people.
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Automated language analysis for Alzheimer’s and frontotemporal dementia across Latino and English-speaking groups
A low-cost, automated language study aims to help diagnose and understand Alzheimer’s and FTD in diverse Spanish- and English-speaking communities.
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Automated language analysis to help diagnose Alzheimer’s and frontotemporal dementia in Latino and English speakers
A low-cost, computer-based method analyzes speech and language to help identify Alzheimer’s disease or frontotemporal dementia in Latino and English-speaking people.
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Automated language analysis for Alzheimer's and frontotemporal dementia in Latino and English-speaking communities
A low-cost, computer-based language analysis method is being developed to help identify Alzheimer’s disease and frontotemporal dementia in Latino and English-speaking people.
-
Automated language analysis for Alzheimer’s disease and frontotemporal dementia in Latino and English speakers
A machine learning approach analyzes speech and language from Latino and English-speaking people to help detect Alzheimer’s disease and related dementias.
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Automated speech and language detection for Alzheimer’s and frontotemporal dementia in Spanish and English speakers
This project uses automated speech and language analysis to help spot signs of Alzheimer’s disease and frontotemporal dementia in Spanish- and English-speaking people, with special focus on Latino communities.
About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page.