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

Fingerprint

Scoring
Disorder
Product-moment correlation
Experimental Analysis
Similarity Measure
Regression Model
Fold
Predict
Evaluate
Prediction
Voice
Language
Repetition
Children

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Asgari, M., Sliter, A., & Van Santen, J. (2016). Automatic scoring of a sentence repetition task from voice recordings. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9924, pp. 470-477). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9924). Springer Verlag. https://doi.org/10.1007/978-3-319-45510-5_54

Automatic scoring of a sentence repetition task from voice recordings. / Asgari, Meysam; Sliter, Allison; Van Santen, Jan.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9924 Springer Verlag, 2016. p. 470-477 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9924).

Research output: Chapter in Book/Report/Conference proceedingChapter

Asgari, M, Sliter, A & Van Santen, J 2016, Automatic scoring of a sentence repetition task from voice recordings. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 9924, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9924, Springer Verlag, pp. 470-477. https://doi.org/10.1007/978-3-319-45510-5_54
Asgari M, Sliter A, Van Santen J. Automatic scoring of a sentence repetition task from voice recordings. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9924. Springer Verlag. 2016. p. 470-477. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-45510-5_54
Asgari, Meysam ; Sliter, Allison ; Van Santen, Jan. / Automatic scoring of a sentence repetition task from voice recordings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9924 Springer Verlag, 2016. pp. 470-477 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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