Abstract
BackgroundSpecific language impairment (SLI) diagnosis is inconvenient due to manual procedures and hardware cost. Computer-aided SLI diagnosis has been proposed to counter these inconveniences. This study focuses on evaluating the feasibility of computer systems used to diagnose SLI.MethodsThe accuracy of Webgazer.js for software-based gaze tracking is tested under different lighting conditions. Predefined time delays of a prototype diagnosis task automation script are contrasted against with manual delays based on human time estimation to understand how automation influences diagnosis accuracy. SLI diagnosis binary classifier was built and tested based on randomised parameters. The obtained results were cross-compared to Singlims_ES.exe for equality.ResultsWebgazer.js achieved an average accuracy of 88.755% under global lighting conditions, 61.379% under low lighting conditions and 52.7% under face-focused lighting conditions. The diagnosis task automation script found to execute with actual time delays with a deviation percentage no more than 0.04%, while manually executing time delays based on human time estimation resulted in a deviation percentage of not more than 3.37%. One-tailed test probability value produced by both the newly built classifier and Singlims_ES were observed to be similar up to three decimal places.ConclusionThe results obtained should serve as a foundation for further evaluation of computer tools to help speech language pathologists diagnose SLI.
Highlights
Specific language impairment (SLI), known as developmental language disorder (DLD) is a disorder which causes delayed language development without physical nor intellectual inhibiting factors
The present study proposes the use of an innovation-driven approach to enhance and semi-automate existing SLI diagnosis procedures
One of the expected outcomes included gaze tracking under low lighting conditions performing with worse accuracy as compared to global lighting
Summary
Specific language impairment (SLI), known as developmental language disorder (DLD) is a disorder which causes delayed language development without physical nor intellectual inhibiting factors. Individuals suffering from SLI experience difficulties producing words verbally, learning new words and making conversation. Being especially common among children and adolescents, SLI affects approximately 7% to 8% of children in kindergarten worldwide, and the problems introduced by SLI can persist into adulthood if it is not diagnosed and treated correctly (1). SLI diagnosis is carried out manually by speech-language pathologists and therapists. Standardised tests involving questionnaires began to be used in schools to screen for cases of language impairments. Specific language impairment (SLI) diagnosis is inconvenient due to manual procedures and hardware cost. Computer-aided SLI diagnosis has been proposed to counter these inconveniences. This study focuses on evaluating the feasibility of computer systems used to diagnose SLI
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