Using brain implants, machine learning and a predictive model, researchers decoded a woman’s attempts to speak at a rate that begins to approach natural conversation.
The sole subject of the Nature study, Bennett, is now 68 years old and has amyotrophic lateral sclerosis, or ALS, a degenerative disease that can eventually cause paralysis. She was a human resources director and equestrian before her disease took hold.
Bennett can move on her own and type with difficulty, but the muscles in her mouth and throat that produce speech no longer work to form intelligible sounds.“When you think of ALS, you think of arm and leg impact,” Bennett wrote in an email, according to a Stanford news release. “But in a group of ALS patients, it begins with speech difficulties. I am unable to speak.
Using a vocabulary of 125,000 words, the team’s system decoded Bennett’s attempted speech at the rate of 62 words per minute, with a 24 percent word-error rate. That is accurate enough to generally get the gist of a sentence, Henderson said, but the authors concluded that the error rate is too high for everyday use and that the system as a whole isn’t yet viable for patients.The authors found that the error rate decreased as more channels — or electrodes reading the brain’s signals — were added.
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