Accentuate the Positive
The automated telephone call centers companies use to reduce costs can drive customers crazy. A way to spot impatience in callers’ voices-and transfer them to human operators before they hang up-could ease the frustration. Shri Narayanan and Chul Min Lee at the University of Southern California have developed a system that distinguishes irritated from normal speech with up to 85 percent accuracy. Their program identifies specific acoustic features of speech that indicate stress, such as the pitch, energy, and duration of speech sounds, as well as word content and contextual information. The system “learned” what to look for through training on nearly 1,400 real phone calls. The team hopes to improve the software’s accuracy but says it could already benefit companies.
Keep Reading
Most Popular
DeepMind’s cofounder: Generative AI is just a phase. What’s next is interactive AI.
“This is a profound moment in the history of technology,” says Mustafa Suleyman.
What to know about this autumn’s covid vaccines
New variants will pose a challenge, but early signs suggest the shots will still boost antibody responses.
Human-plus-AI solutions mitigate security threats
With the right human oversight, emerging technologies like artificial intelligence can help keep business and customer data secure
Next slide, please: A brief history of the corporate presentation
From million-dollar slide shows to Steve Jobs’s introduction of the iPhone, a bit of show business never hurt plain old business.
Stay connected
Get the latest updates from
MIT Technology Review
Discover special offers, top stories, upcoming events, and more.