Summary: Artificial intelligence is now better able to detect depression by analyzing the sound of a person’s voice.
Source: University of Alberta
AI algorithms can now more accurately detect depressed mood using the sound of your voice, according to new research by University of Alberta computing scientists.
The research was conducted by Ph.D. student Mashrura Tasnim and Professor Eleni Stroulia in the Department of Computing Science. The study builds on past research that suggests that the timbre of our voice contains information about our mood. Using standard benchmark data sets, Tasnim and Stroulia developed a methodology that combines several machine-learning algorithms to recognize depression more accurately using acoustic cues.
The ultimate goal, Stroulia explained, is to develop meaningful applications from this technology.
“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.”
Approximately 11 percent of Canadian men and 16 percent of Canadian women will experience major depression in the course of their lives, according to the Government of Canada. And 3.2 million Canadian youth aged 12 to 19 are at risk for developing depression, according to the Canadian Mental Health Association.
Such a tool could prove useful to support work with care providers or to help individuals reflect on their own moods over time. “This work, developing more accurate detection in standard benchmark data sets, is the first step,” added Stroulia.
University of Alberta
Katie Willis – University of Alberta
The image is in the public domain.
Original Research: The study was presented at the Canadian Conference on Artificial Intelligence.
Abstract below by Mashrura Tasnim and Eleni Stroulia.
Detecting Depression from Voice
In this paper, we present our exploration of different machine-learning algorithms for detecting depression by analyzing the acoustic features of a person’s voice. We have conducted our study on benchmark datasets, in order to identify the best framework for the task, in anticipation of deploying it in a future application.