A growing list of management tasks are currently being automated, with the likes of Uber using ‘automated’ management for all of its drivers.  That is perhaps something we can understand when the management is of an analytical nature, but surely algorithms cannot do ‘softer’ tasks can they?

A recent study suggests that such skills may not be beyond algorithms for much longer.  Researchers developed software that was capable of detecting empathy levels in speech.

The software was put through its paces across over 1,000 therapy sessions, with the machine learning algorithm capable of producing an empathy for each session.

Automated empathy ratings

The researchers believe that their automated approach is a marked improvement on current systems that have largely remain untouched over the last 70 years or so.

The concept built upon the burgeoning field of behavioral signal processing, which uses computational methods to aid us in making decisions about behavioral phenomena.

The researchers trained the algorithm to detect empathy within conversations by training it on real data from therapy sessions that tackled alcoholism and other addictions.

A simple level of speech recognition allowed the system to automatically identify key phrases that would signal a level of empathy in the speech.  These included things like “do you think”, and “it sounds like” for high empathy, or “you need to” and “during the past” for low empathy.

“Technological advances in human behavioral signal processing and informatics promise not only scale up and provide cost savings through automation of processes that are typically manual, but enable new insights by offering tools for discovery. This particular study gets at a hidden mental state and what this shows is that computers can be trained to detect constructs like empathy using observational data,” the authors say.

Learning on the fly

As you can perhaps imagine, the system is still in its early stages, so there is much development work to go.  The USC team hope to build in the ability for it to analyze tone of voice, the musicality of our voice and the cadence of our speech in the coming year.

Whilst this work is underway, the team plan to use the tool to help train therapists so that they can behave in a more supportive way towards their clients.

“Being able to assess the quality of psychotherapy is critical to ensuring that patients receive quality treatment”, the team say.

Longer term, the team hope to deploy the tool within therapy sessions to provide a degree of live feedback on how the session is progressing.

What other applications could it have?