TY - GEN
T1 - Automatic scoring of a nonword repetition test
AU - Asgari, Meysam
AU - Van Santen, Jan
AU - Papadakis, Katina
N1 - Funding Information:
This work was supported by NIDCD grants R01DC012033 and R01DC007129. The views herein are those of the authors and do not necessarily reflect the views of the funding agencies.
Publisher Copyright:
© 2017 IEEE.
PY - 2017
Y1 - 2017
N2 - 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.
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.
KW - Autism Spectrum Disorder
KW - Automatic Scoring
KW - Nonword stimuli repetition
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U2 - 10.1109/ICMLA.2017.0-143
DO - 10.1109/ICMLA.2017.0-143
M3 - Conference contribution
AN - SCOPUS:85048510807
T3 - Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017
SP - 304
EP - 308
BT - Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017
A2 - Chen, Xuewen
A2 - Luo, Bo
A2 - Luo, Feng
A2 - Palade, Vasile
A2 - Wani, M. Arif
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017
Y2 - 18 December 2017 through 21 December 2017
ER -