AI assistants will soon recognize and respond to the emotion in your voice
Why it matters to you
AI that can understand how you’re feeling based on the emotion in your voice will open up whole new areas of personalization.
You know when people say that it’s not what you say, but how you say it that matters? Well, very soon that could become a part of smart assistants such as Amazon’s Alexa or Apple’s Siri. At least, it could if these companies decide to use new technology developed by emotion tracking artificial intelligence company Affectiva.
Affectiva’s work has previously focused on identifying emotion in images by observing the way that a person’s face changes when they express particular sentiments. Affectiva’s latest technology builds on that premise through the creation of a cloud-based application program interface (API) that is able to detect emotion in speech. Developed using the power of deep learning technology, the smart tech is capable of observing changes in tone, volume, speed, and voice quality and using this to recognize emotions like anger, laughter, and arousal in recorded speech.
“The addition of Emotion AI for speech builds on Affectiva’s existing emotion recognition technology for facial expressions, making us the first AI company to allow for a person’s emotions to be measured across face and speech,” Rana el Kaliouby, co-founder and CEO of Affectiva, told Digital Trends. “This is all part of a larger vision that we have. People sense and express emotion in many different ways: Through facial expressions, voice, and gestures. We’ve set out to develop multi-modal Emotion AI that can detect emotion the way humans do from multiple communication channels. The launch of Emotion AI for speech takes us one step closer.”
Affectiva developed its voice recognition system by collecting naturalistic speech data from a variety of sources, including commercially available databases. This data was then labeled by human experts for the occurrence of what the company calls “emotion events.” These human generated labels were used to train and validate the team’s deep learning models, so that over time it grew to understand how certain shifts in a person’s voice might indicate a particular emotion.
It’s smart stuff from a technology perspective but, like the best technology, it also has the possibility of helping users on a practical basis. One specific application could include car navigation systems that are able to hear a driver start to experience road rage, and react to prevent them from making a rash driving decision. It could similarly be used to allow automated assistants to change their approach when they hear anger or frustration from a user — or to learn what kind of responses elicit the best reactions and repeat these strategies.