  Specific Language Impairment (SLI) is a disorder that affects communication
and can affect both comprehension and expression. This study focuses on
effectively detecting SLI in children using transcripts of spontaneous
narratives from 1063 interviews. A three-stage cascading pipeline was proposed
f. In the first stage, feature extraction and dimensionality reduction of the
data are performed using the Random Forest (RF) and Spearman correlation
methods. In the second stage, the most predictive variables from the first
stage are estimated using logistic regression, which is used in the last stage
to detect SLI in children from transcripts of spontaneous narratives using a
nearest neighbor classifier. The results revealed an accuracy of 97.13% in
identifying SLI, highlighting aspects such as the length of the responses, the
quality of their utterances, and the complexity of the language. This new
approach, framed in natural language processing, offers significant benefits to
the field of SLI detection by avoiding complex subjective variables and
focusing on quantitative metrics directly related to the child's performance.
