@inbook{e4ef5fe3c8714e46903fd954ea679f0f,
title = "Automatic scoring of a sentence repetition task from voice recordings",
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.",
author = "Meysam Asgari and Allison Sliter and {Van Santen}, Jan",
note = "Funding Information: We thank Katina Papadakis for manually transcribing the corpus for this project. This research was supported by NIH award 1R01DC013996-01A1. Any opinions, findings, conclusions or recommendations expressed in this publication are those of the authors and do not reflect the views of the funding agencies. Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2016.",
year = "2016",
doi = "10.1007/978-3-319-45510-5_54",
language = "English (US)",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer-Verlag",
pages = "470--477",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
}