Because "there are about 100 muscles used to produce speech, and they are controlled by a combination of neurons firing at once," according to New Scientist, "it's not as simple as mapping signals from one electrode to one muscle to sort out what the brain is telling the mouth to do." That's why scientists designed machine learning algorithms to detect brain activity and ultimately produce speech similar to the participant's voice.
The next step was to test speech comprehension. To do this, researchers played the new machine-produced voices to 1,755 native English speakers and asked them to transcribe what they heard.
According to the study, published Wednesday in the journal Nature Neuroscience, the listeners transcribed 43% of the trials perfectly and were able to understand 69% of words spoken on average.
"We still have a ways to go to perfectly mimic spoken language," UCSF researcher Josh Chartier told Newsweek. "We're quite good at synthesizing slower speech sounds like 'sh' and 'z' as well as maintaining the rhythms and intonations of speech and the speaker's gender and identity, but some of the more abrupt sounds like 'b's and 'p's get a bit fuzzy."
Though the two-step process, which involves electrodes to detect brain movement and computer algorithms to reproduce speech, isn't ready for clinical settings, the accuracy produced by their artificial encoder is a significant improvement compared to what's currently available and may prove useful for people who were once able to speak but lost the ability, commonly caused by conditions like Lou Gehrig's disease, autism, some cancers, dementia and other neurological disorders. This is because the device depends on control motor functions, which are still sent to the brain even if an individual is paralyzed.
"People who can't move their arms and legs have learned to control robotic limbs with their brains," Chartier said. "We are hopeful that one day people with speech disabilities will be able to learn to speak again using this brain-controlled artificial vocal tract."