The technology of A.I. Driven voice analysis is very promising for understanding someone’s mental state.
This is why voice analysis might be key to understanding the user’s mental state.
There are various apps on the market that measure stress levels etc. Machine learning researchers are leveraging vocal features to predict depression, anxiety, and even schizophrenia and post-traumatic stress disorder.
All these tools have one thing in common. They’re A.I. Driven. One example is StressWaves. A tool from health care and insurance giant Cigna. The app evaluates stress levels using 60-second samples of recorded speech.
The technology of A.I. Driven voice analysis is very promising for understanding someone’s mental state. It combines the power of A.I. and the use of insightful biomarkers.
The human voice is a rich medium that serves as a primary source for communication. Speech comes very naturally to us. You could say that we are all experts in voice perception.
We use our voice to signal emotions, fears, feelings, and excitation by modulating vocal features such as pitch. The information that we share plays a fundamental role in social interaction.
It makes sense that biomarkers are essential because our mental state and physical state can signal through our voice because they can affect organs such as the heart, lungs, brain, muscles, or vocal folds, which influence our voice.
The interesting thing about this all is that we can measure a lot of important attributes through voice analysis which opens new opportunities for understanding someone’s mental state.
But if we want to create accurate A.I.-driven models, we really need to have a very large and diverse data set. On top of this, there are three important concerns we need to consider.
So again, voice analysis might be key to understanding the user’s mental state. It’s promising, but we need to be careful.