Automatic scoring of a nonword repetition test

Meysam Asgari; Jan Van Santen; Katina Papadakis

doi:10.1109/ICMLA.2017.0-143

Automatic scoring of a nonword repetition test

Meysam Asgari, Jan Van Santen, Katina Papadakis

Institute on Development and Disability

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

In this study, we explore the feasibility of speech-based techniques to automatically evaluate a nonword repetition (NWR) test. NWR tests, a useful marker for detecting language impairment, require repetition of pronounceable nonwords, such as 'D OY F', presented aurally by an examiner or via a recording. Our proposed method leverages ASR techniques to first transcribe verbal responses. Second, it applies machine learning techniques to ASR output for predicting gold standard scores provided by speech and language pathologists. Our experimental results for a sample of 101 children (42 with autism spectrum disorders, or ASD; 18 with specific language impairment, or SLI; and 41 typically developed, or TD) show that the proposed approach is successful in predicting scores on this test, with averaged product-moment correlations of 0.74 and mean absolute error of 0.06 (on a observed score range from 0.34 to 0.97) between observed and predicted ratings.

Original language	English (US)
Title of host publication	Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017
Editors	Xuewen Chen, Bo Luo, Feng Luo, Vasile Palade, M. Arif Wani
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	304-308
Number of pages	5
ISBN (Electronic)	9781538614174
DOIs	https://doi.org/10.1109/ICMLA.2017.0-143
State	Published - 2017
Event	16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017 - Cancun, Mexico Duration: Dec 18 2017 → Dec 21 2017

Publication series

Name	Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017
Volume	2017-December

Other

Other	16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017
Country/Territory	Mexico
City	Cancun
Period	12/18/17 → 12/21/17

Keywords

Autism Spectrum Disorder
Automatic Scoring
Nonword stimuli repetition

ASJC Scopus subject areas

Artificial Intelligence
Computer Science Applications

Access to Document

10.1109/ICMLA.2017.0-143

Cite this

Asgari, M., Van Santen, J., & Papadakis, K. (2017). Automatic scoring of a nonword repetition test. In X. Chen, B. Luo, F. Luo, V. Palade, & M. A. Wani (Eds.), Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017 (pp. 304-308). (Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017; Vol. 2017-December). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICMLA.2017.0-143

Automatic scoring of a nonword repetition test. / Asgari, Meysam; Van Santen, Jan; Papadakis, Katina.
Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017. ed. / Xuewen Chen; Bo Luo; Feng Luo; Vasile Palade; M. Arif Wani. Institute of Electrical and Electronics Engineers Inc., 2017. p. 304-308 (Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017; Vol. 2017-December).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Asgari, M, Van Santen, J & Papadakis, K 2017, Automatic scoring of a nonword repetition test. in X Chen, B Luo, F Luo, V Palade & MA Wani (eds), Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017. Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017, vol. 2017-December, Institute of Electrical and Electronics Engineers Inc., pp. 304-308, 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017, Cancun, Mexico, 12/18/17. https://doi.org/10.1109/ICMLA.2017.0-143

Asgari M, Van Santen J, Papadakis K. Automatic scoring of a nonword repetition test. In Chen X, Luo B, Luo F, Palade V, Wani MA, editors, Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 304-308. (Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017). doi: 10.1109/ICMLA.2017.0-143

Asgari, Meysam ; Van Santen, Jan ; Papadakis, Katina. / Automatic scoring of a nonword repetition test. Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017. editor / Xuewen Chen ; Bo Luo ; Feng Luo ; Vasile Palade ; M. Arif Wani. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 304-308 (Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017).

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AB - In this study, we explore the feasibility of speech-based techniques to automatically evaluate a nonword repetition (NWR) test. NWR tests, a useful marker for detecting language impairment, require repetition of pronounceable nonwords, such as 'D OY F', presented aurally by an examiner or via a recording. Our proposed method leverages ASR techniques to first transcribe verbal responses. Second, it applies machine learning techniques to ASR output for predicting gold standard scores provided by speech and language pathologists. Our experimental results for a sample of 101 children (42 with autism spectrum disorders, or ASD; 18 with specific language impairment, or SLI; and 41 typically developed, or TD) show that the proposed approach is successful in predicting scores on this test, with averaged product-moment correlations of 0.74 and mean absolute error of 0.06 (on a observed score range from 0.34 to 0.97) between observed and predicted ratings.

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ER -

Automatic scoring of a nonword repetition test

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Keywords

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