New research published in BMC Psychiatry finds that changes in speech such as speed, pitch, number of pauses, and intensity can predict who may report more depressive symptoms. The research team found that they could predict with 93% accuracy who would have scores on a measure of depression high enough to be clinically meaningful. This research could lead to new methods for early detection of depression.
Major depressive disorder is one of the most common mental illnesses of our time. it is found all over the world and, according to the WHO, it affects more than 264 million people. A disease that affects so many people can benefit from early detection methods. Research has shown that if early signs of depression are detected, therapeutic interventions can reduce the intensity of the depressive episode. Alexandra König and her colleagues recognize that objective and easy-to-use tools for early identification are needed.
It has been known for some time that those who are depressed speak differently; speed, fluency, and pitch are known to change during depressive episodes. Clinicians report that they look for these speech characteristics during the diagnostic process. If so, König and the research team were curious if a speech analysis test could be developed to look for speech differences in people at risk for depression.
In order to determine if this was possible, subjects without a clinical diagnosis were used in the hope that some would have more depressive symptoms and would be identified by speech analysis. One hundred and eighteen university students were recruited for the study. First, participants took an assessment called “Trail Making”. This assessment aimed to measure their cognitive speed to solve problems. Next, they assessed depressive symptoms; then they were recorded talking.
The speaking task asked them to talk for one minute about something positive in their life and one minute about something negative. The speech task was analyzed, looking for specific acoustic characteristics, the number of words spoken and the number of words spoken in a speech segment (before a pause).
Their results revealed that 25 of their subjects scored high enough on the measure of depression to be considered for a clinical diagnosis of depression. These 25 subjects spoke more words than those who did not score high for depression, and this was true in both positive and negative stories. Additionally, speech speed, pitch, and prosodic features of speech were strong predictors of who would have depression scores. Finally, those with high depression scores took longer to complete the Trail Making Test.
The research team recognizes certain limitations to their work. Their speech recording was short, just two minutes per topic, which might have taken longer to make reliable predictions. Second, the subjects of their study were all university students, making the sample unrepresentative. Finally, the subjects were not clinically observed, so it is impossible to know if they would have been diagnosed with clinical depression.
Despite these limitations, the research team finds their work valuable in pursuing the early detection of depressive symptoms. They conclude: “Taken together, our study adds to the current literature that speech features are sensitive for the detection of depressive symptoms even in a non-clinical sample.”
The study, “Detection of subtle signs of depression with automated speech analysis in a non-clinical sample,” was authored by Alexandra König, Johannes Tröger, Elisa Mallick, Mario Mina, Nicklas Linz, Carole Wagnon, Julia Karbach, Caroline Kuhn and Jessica Pierre.