In human communication, emotion plays a pivotal role. It adds depth, context and nuance to our interactions, significantly impacting how we understand and respond to each other. While humans are adept at discerning emotions from voice cues, machines are now also honing this capability, thanks to the advancement of machine learning algorithms. This interesting news came to us from PsyPost in their article, “Machine learning tools can predict emotion in voices in just over a second.”

Machine learning, a subset of artificial intelligence (AI), empowers computers to learn patterns from data and make predictions or decisions without explicit programming. In recent years, machine learning has made significant strides in understanding and interpreting human emotions, particularly from voice inputs.

Detecting emotions from speech is a complex task, as it involves analyzing various acoustic features such as pitch, intensity, tempo and spectral characteristics. These features vary depending on the emotional state of the speaker.

By deciphering the subtle nuances of human emotion conveyed through speech, these systems have the potential to revolutionize various aspects of human interaction, from customer service to mental health monitoring. However, responsible deployment and ethical considerations must accompany the development and implementation of such technologies to ensure they benefit society while upholding individuals’ rights and well-being.

The real challenge is that most organizations have little knowledge on how AI systems make decisions. Explainable AI allows users to comprehend and trust the results and output created by machine learning algorithms.

Melody K. Smith

Data Harmony is an award-winning semantic suite that leverages explainable AI.

Sponsored by Access Innovations, the intelligence and the technology behind world-class explainable AI solutions.