Alberta-made technology screens people’s speech for early signs of Alzheimer’s

Alberta technology is using audio cues to help detect Alzheimer's disease. It listens for three features: pauses in speech, word length or complexity, and speech intelligibility.

Alberta researchers have found a way to catch potential early signs of Alzheimer’s disease.

They’re using a machine-learning model to detect audio cues — certain speech patterns that are linked to a diagnosis of Alzheimer’s or other forms of dementia.

“We’re interested in looking at speech in particular as a window into the human mind, so to speak,” said Zehra Shah, a University of Alberta graduate student and lead researcher.

“The idea here is we want to look at speech as a potential biomarker in order to be able to identify patterns that might help us diagnose and monitor psychiatric disorders such as Alzheimer’s dementia.”

The technology listens for three features: pauses in speech, word length or complexity, and speech intelligibility.

“For dementia patients, because there might be a need for more recall, they tend to forget words and they need a certain amount of time to recall those words, so there will be longer pauses,” Shah explained.

“A longer word, we assume, will have a higher degree of speech complexity rather than shorter words like ‘uh’ and ‘the.’

“Longer word duration … is a proxy for speech complexity,” she added. “Again, the hypothesis here is that dementia patients would have lower speech complexity compared to healthy controls.”