Neuroengineers have created a system that translates thought into intelligible, recognizable speech. By monitoring someone’s brain activity, the technology can reconstruct the words a person hears with unprecedented clarity. This breakthrough, which harnesses the power of speech synthesizers and artificial intelligence, could lead to new ways for computers to communicate directly with the brain.
It also lays the groundwork for helping people who cannot speak, such as those living with amyotrophic lateral sclerosis (ALS) or recovering from stroke, regain their ability to communicate with the outside world.
“Our voices help connect us to our friends, family and the world around us, which is why losing the power of one’s voice due to injury or disease is so devastating,” said Nima Mesgarani, PhD, the paper’s senior author and a principal investigator at Columbia University’s Mortimer B. Zuckerman Mind Brain Behavior Institute. “With today’s study, we have a potential way to restore that power. We’ve shown that, with the right technology, these people’s thoughts could be decoded and understood by any listener.”
Decades of research has shown that when people speak — or even imagine speaking — telltale patterns of activity appear in their brain. Distinct (but recognizable) patterns of signals also emerge when we listen to someone speak, or imagine listening. Experts, trying to record and decode these patterns, see a future in which thoughts need not remain hidden inside the brain — but instead could be translated into verbal speech at will.
To teach the vocoder to interpret to brain activity, they asked epilepsy patients already undergoing brain surgery to listen to sentences spoken by different people, while they measured patterns of brain activity. The neural patterns trained the vocoder.
Next, the researchers asked those same patients to listen to speakers reciting digits between 0 to 9, while recording brain signals that could then be run through the vocoder. The sound produced by the vocoder in response to those signals was analyzed and cleaned up by neural networks, a type of artificial intelligence that mimics the structure of neurons in the biological brain.
The end result was a robotic-sounding voice reciting a sequence of numbers. To test the accuracy of the recording, they tasked individuals to listen to the recording and report what they heard. They found that people could understand and repeat the sounds about 75 percent of the time, which is well above and beyond any previous attempts. Ultimately, they hope their system could be part of an implant, similar to those worn by some epilepsy patients, that translates the wearer’s thoughts directly into words.