The study, conducted by PhD student Mashrura Tasnim and Professor Eleni Stroulia of the Department of Computing Science, builds on past research which suggests that the timbre of our voice contains information about our mood.
By using standard benchmark data sets, Tasnim and Stroulia developed a methodology that combines several machine-learning algorithms to recognise depression more accurately using acoustic cues.
The ultimate goal of the study is to develop meaningful applications from this technology, Stroulia explained.
“A realistic scenario is to have people use an app that will collect voice samples as they speak naturally,” she said. “The app, running on the user’s phone, will recognise and track indicators of mood, such as depression, over time.”
“Much like you have a step counter on your phone,” she added, “you could have a depression indicator based on your voice as you use the phone.”
According to the government of Canada, approximately 11 per cent of Canadian men and 16 per cent of Canadian women will experience major depression in the course of their lives.
Furthermore, 3.2 million Canadians aged between 12-19 are at risk of developing depression, according to research from the Canadian Mental Health Association.
Such a tool could potentially prove useful to support work with care providers or to help individuals reflect ton their own moods over time.
“This work, developing more accurate detection in standard benchmark data sets, is the first step,” Stroulia added.
Aidan Jones, chief executive of Relate, recently said developments in the potential of digital assistive technology to help treat a range of mental health issues is growing, with AI proving to be a foundation technology for bots designed for ‘human-like’ interaction with individuals.