Automatic scoring of a Sentence Repetition Task from Voice Recordings.
Text Speech Dialog
; 9924: 470-477, 2016 Sep.
Article
en En
| MEDLINE
| ID: mdl-33244525
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.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Tipo de estudio:
Prognostic_studies
Idioma:
En
Revista:
Text Speech Dialog
Año:
2016
Tipo del documento:
Article
País de afiliación:
Estados Unidos
Pais de publicación:
ALEMANHA
/
ALEMANIA
/
DE
/
DEUSTCHLAND
/
GERMANY