Automatic scoring of a sentence repetition task from voice recordings

Meysam Asgari, Allison Sliter, Jan Van Santen

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

In this paper, we propose an automatic scoring approach for assessing the language deficit in a sentence repetition task used to evaluate children with language disorders. From ASR-transcribed sentences, we extract sentence similarity measures, including WER and Levenshtein distance, and use them as the input features in a regression model to predict the reference scores manually rated by experts. Our experimental analysis on subject-level scores of 46 children, 33 diagnosed with autism spectrum disorders (ASD), and 13 with specific language impairment (SLI) show that proposed approach is successful in prediction of scores with averaged product-moment correlations of 0.84 between observed and predicted ratings across test folds.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages470-477
Number of pages8
Volume9924
DOIs
StatePublished - 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9924
ISSN (Print)03029743
ISSN (Electronic)16113349

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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