Abstract
Specific Language Impairment (SLI) is a language disorder that delays progress in mastering speech-language skills, and typically occurs in childhood. Most speech-language pathologists commonly use paper-based instruments to diagnose and treat this problem. This article describes the design, implementation, and evaluation of SATEL, an ontology-based system used both in diagnosing this condition and as part of speech therapy for children with SLI. With the help of a Kinect sensor, SATEL is able to recognize and classify pronounced words. The proposed system was designed and evaluated by a team of four speech-language pathologists and 26 children diagnosed with SLI. Results showed an accuracy rate of 94.42% and 97.75% in recognizing syllables and words correctly and incorrectly pronounced in the diagnostic and treatment modules, respectively.
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