Alberta-made technology screens people’s speech for early signs of Alzheimer’s | 24CA News
Alberta researchers have discovered a method to catch potential early indicators of Alzheimer’s illness.
They’re utilizing a machine-learning mannequin to detect audio cues — sure speech patterns which can be linked to a analysis of Alzheimer’s or different types of dementia.
“We’re interested in looking at speech in particular as a window into the human mind, so to speak,” stated Zehra Shah, a University of Alberta graduate pupil 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 know-how listens for 3 options: pauses in speech, phrase size 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 defined.
“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.”
Researchers used 250 English-speaking people — half have been labeled as dementia sufferers and half have been a management inhabitants.
The mannequin was capable of distinguish Alzheimer’s sufferers from wholesome controls with 70-75 per cent accuracy.
“It’s like a support tool for clinical diagnosis,” Shah stated. “But we don’t foresee this tool to be a diagnostic tool in and of itself. It would need a human in the loop.
“It’s the first point, triaging, screening for potentially at-risk populations to see where they are at this point in time and possibly flagging any higher-risk individuals in this category and asking them to look into further screening.”
It’s not supposed to exchange scientific analysis. But Shah hopes the know-how will ultimately result in widespread simple and common entry to an early detection device, obtainable to anybody with a smartphone.
The venture continues to be in its early levels however Shah thinks it has nice potential and may very well be formatted for an app.
“Which would not be monitoring continuously but you can open the app and speak into it. For example, you could have the app ask you on a daily basis: ‘How is your day going?’ and the person just responds in a spontaneous manner and the app could, in the background, potentially look at features in your speech to see how it’s changed.”
There are additionally alternatives for this technique to extend entry to well being care.
“We find that speech as a biomarker is really interesting, looking at it from a remote mental-health-care perspective,” Shah stated. “We can think about the potential of using this kind of technology for tele-health, remote mental health monitoring.”
And, it may very well be broadly utilized in any language. Since it’s not listening to particular phrases — however moderately pauses and phrase size.
“We are looking at speech samples without looking at the actual language content since we’re looking at features that would work across different languages and so we’re not really focused on the word content but we’re looking at other features,” Shah stated.
“We’re looking at a language-agnostic tool that can do the same thing. We would like for this technology to be utilized across a slew of different languages, so we’re not restricted to the English language any longer and so that’s where the potential lies for scalability as well.”
The machine-learning mannequin was described in a paper, “Exploring Language-Agnostic Speech Representations Using Domain Knowledge for Detecting Alzheimer’s Dementia.”
The analysis staff ranked first in North America and fourth globally within the ICASSP 2023 Signal Processing Grand Challenge.
Speech-based machine studying mannequin for dementia. University of Alberta.
Courtesy: Zehra Shah
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