Evaluating NLP Features for Automatic Prediction of Language Impairment Using Child Speech Transcripts

Language impairment (LI) in children is pervasive in all walks of life. Automatic prediction of LI is useful as a first pass for speech language pathologists in identifying prospective children with LI. Previous work in the automatic prediction of LI has explored various features, mostly shallow and surface level features. In this paper, we evaluate deeper Natural Language Processing (NLP) features such as syntactic, semantic and entity grid model features, along with narrative structure and quality features in the prediction of LI using child language transcripts. Our experiments show that narrative structure and quality features along with a combination of other features are helpful in the prediction of LI in storytelling narratives.