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.
Toronto wants to kill the smart city forever
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Saudi Arabia plans to spend $1 billion a year discovering treatments to slow aging
The oil kingdom fears that its population is aging at an accelerated rate and hopes to test drugs to reverse the problem. First up might be the diabetes drug metformin.
Yann LeCun has a bold new vision for the future of AI
One of the godfathers of deep learning pulls together old ideas to sketch out a fresh path for AI, but raises as many questions as he answers.
The dark secret behind those cute AI-generated animal images
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