In the latest study led by a team of computing scientists at the University of Alberta, AI algorithms will detect depressed mode by analyzing sound of the participant’s voice.
According to reports, the research was led by PhD student Mashrura Tasnim and the professor Eleni Stroulia from the Department of Computing Science, and is based on findings from previous led studies that claimed the timbre of human voice held the crucial information about the person’s mood.
With the help of standard data sets, the team worked on a methodology which combined various machine-learning algorithms that helped in detecting depression more accurately with regards to acoustic cues. The researchers focused on creating significant applications from the technology.
As predicted by a report released by the Government of Canada, approximately 11 per cent of Canadian men and 16 per cent of Canadian women are likely to witness major incidence of depression through the course of their lives. Furthermore, owning to report published by Canadian Mental Health Association, 3.2 million Canadian youth belonging to age group of 12 to 19 were also at risk of developing depression.
The breakthrough research was thus claimed to be advantageous to care providers as well as to individuals who can reflect on their mood over the course of time. The possibility of accurate detection by means of the new technology is one of the first steps in standard benchmark data sets, as quoted by Professor Eleni Stroulia.
Moreover, Stroulia while speaking about the practical use of the new technology added, “A realistic scenario is to have people use an app that will collect voice samples as they speak naturally. 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.”