Specific language impairment (SLI) detection pipeline from transcriptions of spontaneous narratives
This provides a quantitative, objective method for SLI detection in children, avoiding subjective assessments, though it appears incremental as it combines existing techniques like Random Forest and logistic regression.
The study tackled detecting Specific Language Impairment (SLI) in children using transcripts of spontaneous narratives from 1063 interviews, achieving 97.13% accuracy with a three-stage pipeline involving feature extraction, logistic regression, and a nearest neighbor classifier.
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.