Successful drug development for Alzheimer’s disease (AD) depends on clinicians’ ability to diagnose and quantify disease severity and progression—especially via clear, measurable biomarkers that can detect subtle changes in patients’ pathologic cognitive capacities and neuronal decline long before they show other, more serious symptoms. Alterations of speech and language are showing promise as possible early biomarkers of AD.

Researchers can collect and analyze speech and language information using new and improved technology, hardware and data analytics. Likewise, ubiquitous use of smart devices enables remote data collection, both active (prompted by the user) and passive (without user prompts). These tools can measure acoustic features such as pitch and amplitude, as well as lexical and syntactic aspects of speech and features of written language such as text contextual or semantic information—all of which are associated with early AD and its progression.

Yet researchers have not been able to fully take advantage of the opportunities these tools can offer. To optimize speech and language biomarker discovery, researchers need a comprehensive speech-sample repository that covers a large, diverse cohort of subjects representing different accents, languages, speech and language components, and disease stages. They also need state-of-the-art participant characterization along with harmonized protocols and standards that cover the types of speech and language samples. These activities are nearly impossible for most research groups or startups to achieve on their own due to the costs associated with participant characterization (such as repeated PET scans, MRIs, and blood-based biomarkers in large longitudinal cohorts).

This protocol engages a global partnership between clinicians, researchers and data scientists to meet these challenges, facilitating further identification, development and validation of speech-based biomarkers to enable researchers to apply artificial-intelligence algorithms for AD screening, detection, prediction, diagnosis, and monitoring. Existing consortia in related fields demonstrate that global collaboration and data sharing can indeed produce meaningful results.