Automatic Assessment of Language Ability in Children with and without Typical Development
- Resource Type
- Conference
- Authors
- Gale, Robert; Dolata, Jill; Prud'hommeaux, Emily; van Santen, Jan; Asgari, Meysam
- Source
- 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) Engineering in Medicine & Biology Society (EMBC), 2020 42nd Annual International Conference of the IEEE. :6111-6114 Jul, 2020
- Subject
- Bioengineering
Task analysis
Atmospheric measurements
Particle measurements
Hidden Markov models
Predictive models
Standards
Gold
- Language
- ISSN
- 2694-0604
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.