Prediction of Specific Language Impairment in Children Using Speech Linear Predictive Coding Coefficients

Specific language impairment (SLI) is a kind of neurodevelopmental disorders which could disturb the speech production and language comprehension ability of a child. The detection/prediction of SLI in children is done through behavioral analysis and age specific language test scores by speech therapists and child psychologists. Modern computational approaches may chop off the long prediction time to diagnose the disorder and support the child as well as parents to start ministrations early. The present work gives an idea for detection of SLI from children's voice with the help of linear predictive coding coefficients (LPCs) as they have the ability to model human vocal system. Two classifiers: naïve-Bayes (NB) and support vector machine (SVM) were resorted to categorize children in both SLI and healthy groups respectively. The best classification accuracies of 97.9% (for top-20 LPC features) and 97.8% (for top-10 LPC features) were secured by NB classifier for 5-fold cross-validation protocol. The study concluded that LPC parameters play a crucial role in detection of SLI and could be put to uncover other discern neurodevelopmental disorders via children's speech signals.