Researchers at the University of California, Berkeley, and UC San Francisco have achieved a significant breakthrough in brain-computer interface (BCI) technology. They have developed a system that can translate neural signals from speech centers in the brain into synthetic speech in near real-time. This innovation marks a critical step toward restoring naturalistic communication for individuals with severe paralysis.
The new BCI uses AI-based modeling to synthesize brain signals into audible speech with minimal latency. In a study published in Nature Neuroscience, the researchers demonstrated that their streaming method enables near-synchronous voice streaming, resulting in more fluent and natural-sounding speech synthesis. The technology works by sampling neural data from the motor cortex, the area of the brain responsible for speech production, and then uses AI to decode brain function into speech.
This advancement addresses the challenge of latency in previous speech neuroprostheses, significantly reducing the time lag between a person's attempt to speak and the production of audible speech. The improved speed and accuracy of the new BCI hold tremendous potential for enhancing the quality of life for people living with paralysis affecting speech, offering a more natural and seamless communication experience.