Exploring a corpus-based approach for detecting language impairment in monolingual English-speaking children

This paper explores the use of an automated method for analyzing narratives of monolingual English speaking children to accurately predict the presence or absence of a language impairment. The goal is to exploit corpus-based approaches inspired by the fields of natural language processing and machine learning.

We extract a large variety of features from language samples and use them to train language models and well known machine learning algorithms as the underlying predictors. The methods are evaluated on two different datasets and three language tasks. One dataset contains samples of two spontaneous narrative tasks performed by 118 children with an average age of 13 years and a second dataset contains play sessions from over 600 younger children with an average age of 6 years.

We compare results against a cut off baseline method and show that our results are far superior, reaching F-measures of over 85% in two of the three language tasks, and 48% in the third one.

The different experiments we present here show that corpus based approaches can yield good prediction results in the problem of language impairment detection. These findings warrant further exploration of natural language processing techniques in the field of communication disorders. Moreover, the proposed framework can be easily adapted to analyze samples in languages other than English since most of the features are language independent or can be customized with little effort.