Not being able to communicate with loved ones is an unfortunate reality for some, such as recent stroke patients or mute individuals. Vocal imaging research performed by Professor Ahmed Sabbir Arif’s lab is contributing to a new form of communication that could help these afflicted populations communicate, even if they can’t audibly express themselves.
Arif runs the Human Computer Interaction Group where he and fourth-year doctoral student Laxmi Pandey recently developed a deep neural net framework that translates real-time magnetic resonance imaging (MRIs) of vocal tract shaping to text. The lab used a data set of MRI images that shows the vocal movements people make when speaking.
“The MRI images are of the vocal cords and surrounding areas where there are a lot of muscles that are used to produce speech,” Arif said. “The idea is that by processing these images, we are able to understand those who are unable to produce speech.”
The human-computer field is a collaborative one, with researchers and universities openly sharing their findings. Because of this, Arif was able to process thousands of MRI images from the University of Southern California on the campus’s cluster, or supercomputer. Arif’s lab taught the computer words and sentences based on the shown articulatory vocal patterns with an accuracy rate of 60 percent.
“We found incredible results considering this is the first time someone has done something like this,” Arif said of the study.
Professor Ahmed Sabbir Arif’s lab is contributing to a new form of communication that could people who are not able to audibly express themselves.
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October 20, 2021 at 06:14AM
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