July 11, 2019 1:50 pm
Updated: July 12, 2019 3:58 pm

Alberta researchers use AI to detect depression in voices

Alberta research is using the timbre in a person's voice to track depression and mood over time.

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Artificial intelligence algorithms can now more accurately detect depressed moods based on the sound of a person’s voice, thanks to research by University of Alberta computer scientists.

The study used previous research that suggests the timbre of a person’s voice contains information about their mood.

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PhD student Mashrura Tasnim and professor Eleni Stroulia developed a methodology that combines several machine-learning algorithms to more accurately detect depression using acoustic cues.

The pair sees the technology being applied by individuals and care providers.

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“A realistic scenario is to have people use an app that will collect voice samples as they speak naturally,” Stroulia said.

“The app, running on the user’s phone, will recognize and track indicators of mood, such as depression, over time.

“Much like you have a step counter on your phone, you could have a depression indicator based on your voice as you use the phone,” she said.

LISTEN BELOW: Dr. Eleni Stroulia joins the Ryan Jespersen Show

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Federal government data shows about 11 per cent of Canadian men and 16 per cent of Canadian women will experience a major depression sometime in their life. More than three million youth aged 12 to 19 are at risk of developing depression, according to the Canadian Mental Health Association.

READ MORE: ‘Depression isn’t like a broken bone’: Steps to overcome this common mental illness

Stroulia said this type of research helps establish baseline data on depression.

“This work, developing more accurate detection in standard benchmark data sets, is the first step,” she said.

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The paper, Detecting Depression from Voice, was presented at the Canadian Conference on Artificial Intelligence.

Watch below (June 25, 2019): Depression doesn’t just affect the mind. Here are some of the physical symptoms of being depressed.

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