Empathy is one of the most important qualities to look for in a therapist. But how can you know if your therapist has this trait? Technology developed by researchers from the University of Southern California (USC), University of Washington, and the University of Utah can tell you.
Utilizing new developments in automatic speech recognition, natural language processing, and machine learning, researchers developed software to detect “high-empathy” or “low-empathy” speech by analyzing more than 1,000 therapist-patient sessions.
This is the first known study to record therapy sessions and automatically determine the quality of a therapy session based on a single characteristic. The findings are published in the December issue of PLoS ONE.
Currently, there are very few ways to assess the quality of a therapy session. In fact, according to the researchers, the methods for evaluating therapy have remained unchanged for 70 years. Methods requiring third-party human evaluators are time-consuming and affect the privacy of each session.
Instead, imagine a natural language processing app like SIRI listening in for the right phrases and vocal qualities. The researchers taught their algorithm to recognize empathy using data from training sessions for therapists, specifically looking at therapeutic interactions with individuals coping with addiction and alcoholism.
Using automatic speech recognition and machine learning-based models, the algorithm then automatically identified select phrases that would indicate whether a therapist demonstrated high or low empathy.
Key phrases such as: “it sounds like,” “do you think,” and “what I’m hearing,” indicated high empathy, while phrases such as “next question,” “you need to,” and “during the past,” were perceived as low-empathy by the computational model.
The research team’s Signal Analysis and Interpretation Lab at USC continues to develop more advanced models; giving the algorithm the capacity to analyze diction, tone of voice, the musicality of one’s speech (prosody) as well as how the cadence of one speaker in conversation is echoed with another (for example when a person talks fast and the listener’s oral response mirrors the rhythm with quick speech).
In the near future, the researchers are hoping to use this tool to train new therapists.
“Being able to assess the quality of psychotherapy is critical to ensuring that patients receive quality treatment,” said David Atkins, Ph.D., a University of Washington research professor of psychiatry.
In the long run, the researchers hope to develop software that provides real-time feedback or can rate a therapy session on the spot. In addition, the researchers want to incorporate additional elements into their empathy rating algorithm, including acoustic channels and the frequency with which a therapist or patient speaks.
“The sort of technology our team of engineers and psychologists is developing may offer one way to help providers get immediate feedback on what they are doing — and ultimately improve the effectiveness of mental health care,” said Zac Imel, Ph.D., a University of Utah professor of educational psychology and the paper’s corresponding author.