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By David Szondy, December 7, 2012
The app analyzes twelve features of speech such as pitch and
volume and uses this to identify one of six emotional states (Photo: Shutterstock)
Image Gallery (3 images)
Image Gallery (3 images)
It would be great if smartphones could sense moods – especially
when they've dropped a call three times in five minutes. Engineers at
the University of Rochester in Rochester, New York have developed a
prototype app that provides phones with a form of emotional intelligence
that could have wide applications in phones and beyond.
Described by the team on December 5 at the IEEE Workshop on Spoken Language Technology, Miami, Florida, the app analyzes twelve features of speech such as pitch and volume and uses this to identify one of six emotional states.
Though there are other emotion evaluating systems, the University of Rochester approach is surprisingly simple. Instead of studying a speaker’s gestures and expression or trying to figure out the meaning of words or the structure of conversation, the Rochester system deals strictly with the inherent voice cues that people use to pick up on emotion. It’s a bit like how you can figure out the mood of someone on the phone from Kenya even though you don’t speak a word of Swahili.
"We actually used recordings of actors reading out the date of the
month – it really doesn't matter what they say, it's how they're saying
it that we're interested in," explained Wendi Heinzelman, professor of
electrical and computer engineering. The actors would read the words
with various mood expression in their voices. The engineers then had the
system analyze twelve features of speech and assign them with one of
six emotions - "sad," "happy," "fearful," "disgusted," or "neutral."
This turned out to be very accurate with a correct emotional
identification 81 percent of the time as opposed to the 55 percent found
in other systems.
The team has built a prototype app from the system that displays a
happy or sad face depending on the speaker’s mood. "The research is
still in its early days," said Heinzelman, "but it is easy to envision a
more complex app that could use this technology for everything from
adjusting the colors displayed on your mobile to playing music fitting
to how you're feeling after recording your voice."
Another application of the technology would be for psychologists. "A reliable way of categorizing emotions could be very useful in our research," says collaborating Rochester psychologist Melissa Sturge-Apple. "It would mean that a researcher doesn't have to listen to the conversations and manually input the emotion of different people at different stages."
Currently, the technology is highly speaker dependent. Like many speech recognition systems, they work better when the system is trained for a particular person. When its used with someone else, the accuracy rate drops to 30 percent. The team is now working to train the system with voices of people in the same age group and of the same gender to try to make it more practical in real-life situations.
Source: University of Rochester
Described by the team on December 5 at the IEEE Workshop on Spoken Language Technology, Miami, Florida, the app analyzes twelve features of speech such as pitch and volume and uses this to identify one of six emotional states.
Though there are other emotion evaluating systems, the University of Rochester approach is surprisingly simple. Instead of studying a speaker’s gestures and expression or trying to figure out the meaning of words or the structure of conversation, the Rochester system deals strictly with the inherent voice cues that people use to pick up on emotion. It’s a bit like how you can figure out the mood of someone on the phone from Kenya even though you don’t speak a word of Swahili.
Another application of the technology would be for psychologists. "A reliable way of categorizing emotions could be very useful in our research," says collaborating Rochester psychologist Melissa Sturge-Apple. "It would mean that a researcher doesn't have to listen to the conversations and manually input the emotion of different people at different stages."
Currently, the technology is highly speaker dependent. Like many speech recognition systems, they work better when the system is trained for a particular person. When its used with someone else, the accuracy rate drops to 30 percent. The team is now working to train the system with voices of people in the same age group and of the same gender to try to make it more practical in real-life situations.
Source: University of Rochester
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