Researchers at The
The multidisciplinary effort will focus primarily on improving speech communication, predicting verbal deficits in patients with amyotrophic lateral sclerosis and improving diagnostic testing for speech disorders in children.
The projects will be through the UT
Campbell and his colleagues received a small-business grant to develop software that uses the data from electromagnetic articulography to create a computer-generated representation of a person's tongue movements. Electromagnetic articulography detects the movements of oral sensors in an electrostatic field. The display would provide patients with a real-time view of how they are moving their tongues when they talk, which could help them to improve their speech.
"It'd be like trying to grab for a cup without being able to see your hand. All you know is you didn't get it," Katz said about speech training without visual feedback. "Our approach is to give the patient additional real-time information about tongue movement during speech. The goal is to improve their tongue positioning behavior through self-correction and practice."
A second grant will use the same electromagnetic articulography to predict the progression of ALS, also known as
As ALS progresses, the motor neurons enabling speech eventually die, thus robbing the patient of the ability to speak. Using the electromagnetic articulography, researchers hope to improve their ability to reliably monitor changes in the tongue movements in ALS patients. This information may allow future software to recognize intended words and create an avenue for continued communication.
The last grant focuses on developing new measures for diagnosing speech disorders in young children. Knowing specifically how a child is having difficulty producing speech may give clinicians a better chance to develop targeted treatments.
Campbell and his colleagues will test new software developed to aid speech-language pathologists in the diagnosis process. The main test for the software is whether it will detect the subtle speech characteristics that indicate different speech disorders, such as the length of pauses between words or the accuracy of diction.
Speech sounds recorded from 200 children will be submitted to the software, which will conduct spectral and temporal analysis on the sounds to predict disorders. By comparing the computer analysis to clinicians' diagnoses, the researchers hope to offer refinements to the software to increase its reliability and accuracy. Eventually, they hope the software will automate the diagnosis process for clinicians.
Co-researchers on the projects will include Dr.
TNS 30TagarumaMar-140708-4790128 30TagarumaMar
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