Specific language impairment (SLI) detection pipeline from transcriptions of spontaneous narratives

TL;DR

This paper presents a natural language processing pipeline that detects Specific Language Impairment in children with high accuracy by analyzing transcripts of spontaneous narratives, emphasizing quantitative linguistic features.

Contribution

The study introduces a novel three-stage cascading pipeline combining feature selection, dimensionality reduction, and classification for SLI detection from narrative transcripts.

Findings

Achieved 97.13% accuracy in SLI detection

Identified key linguistic features like response length and language complexity

Demonstrated effectiveness of NLP-based quantitative metrics

Abstract

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.