TY - GEN
T1 - Automatic Assessment of Language Ability in Children with and without Typical Development
AU - Gale, Robert
AU - Dolata, Jill
AU - Prud'Hommeaux, Emily
AU - Van Santen, Jan
AU - Asgari, Meysam
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/7
Y1 - 2020/7
N2 - This study describes a fully automated method of expressive language assessment based on vocal responses of children to a sentence repetition task (SRT), a language test that taps into core language skills. Our proposed method automatically transcribes the vocal responses using a test-specific automatic speech recognition system. From the transcriptions, a regression model predicts the gold standard test scores provided by speech-language pathologists. Our preliminary experimental results on audio recordings of 104 children (43 with typical development and 61 with a neurodevelopmental disorder) verifies the feasibility of the proposed automatic method for predicting gold standard scores on this language test, with averaged mean absolute error of 6.52 (on a observed score range from 0 to 90 with a mean value of 49.56) between observed and predicted ratings.Clinical relevance - We describe the use of fully automatic voice-based scoring in language assessment including the clinical impact this development may have on the field of speech-language pathology. The automated test also creates a technological foundation for the computerization of a broad array of tests for voice-based language assessment.
AB - This study describes a fully automated method of expressive language assessment based on vocal responses of children to a sentence repetition task (SRT), a language test that taps into core language skills. Our proposed method automatically transcribes the vocal responses using a test-specific automatic speech recognition system. From the transcriptions, a regression model predicts the gold standard test scores provided by speech-language pathologists. Our preliminary experimental results on audio recordings of 104 children (43 with typical development and 61 with a neurodevelopmental disorder) verifies the feasibility of the proposed automatic method for predicting gold standard scores on this language test, with averaged mean absolute error of 6.52 (on a observed score range from 0 to 90 with a mean value of 49.56) between observed and predicted ratings.Clinical relevance - We describe the use of fully automatic voice-based scoring in language assessment including the clinical impact this development may have on the field of speech-language pathology. The automated test also creates a technological foundation for the computerization of a broad array of tests for voice-based language assessment.
UR - http://www.scopus.com/inward/record.url?scp=85091023199&partnerID=8YFLogxK
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U2 - 10.1109/EMBC44109.2020.9175264
DO - 10.1109/EMBC44109.2020.9175264
M3 - Conference contribution
AN - SCOPUS:85091023199
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 6111
EP - 6114
BT - 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020
Y2 - 20 July 2020 through 24 July 2020
ER -