Speech analyser could reveal mental health

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New York, Nov 2 (IANS) A programme that analyses speech and uses it to gain information about one’s mental health is in the works.

The idea to develop such a device came when researchers, including an Indian-origin scientist, from the University of Maryland in the US found that certain vocal features change as patients’ feelings of depression worsen.
“This system could monitor both physical and psychological symptoms of mental illness on a regular basis and provide both patients and their mental health providers with feedback about their status,” the authors said.
To conduct a quantitative experiment on the vocal characteristics of depression, acoustician Carol Espy-Wilson and her colleagues re-purposed a dataset collected from a 2007 study from an unaffiliated lab also investigating the relationship between depression and speech patterns.
The researchers used data from six patients, who over the six-week course of the previous study had registered as being depressed for some weeks and being not depressed for a few.
They compared the speech patterns of these patients each week and found a correlation between depression and certain acoustic properties.
When patients’ feelings of depression were worst, their speech tended to be breathier and slower.
“We also found increases in jitter and shimmer — two measures of acoustic disturbance that measure the frequency and amplitude variation of the sound, respectively. Speech high in jitter and shimmer tends to sound hoarse or rough,” Espy-Wilson added.
The researchers plan to repeat the study in a larger population, comparing speech patterns in individuals with no history of mental illness to those with depression to create an acoustic profile of depression-typical speech.
A phone app could use this information to analyse patients’ speech, identify acoustic signatures of depression and provide feedback and support.
The team presented the findings at the 168th meeting of the Acoustical Society of America (ASA) in Indianapolis.

Last modified: December 22, 2014