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AUTOMATIC SCORING OF A NONWORD REPETITION TEST.

Meysam Asgari¹, Jan Van Santen¹, Katina Papadakis¹

¹Center for Spoken Language Understanding, Institute on Development & Disability, Oregon Health & Science University.

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|November 20, 2020

Summary

This summary is machine-generated.

This study shows that automated speech technology can accurately score nonword repetition (NWR) tests, a key indicator for language impairment. This method offers a feasible way to evaluate NWR test performance in children.

Keywords:

Autism Spectrum Disorder Automatic Scoring Nonword stimuli repetition

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Assessing Working Memory in Children: The Comprehensive Assessment Battery for Children – Working Memory (CABC-WM)

Assessing Working Memory in Children: The Comprehensive Assessment Battery for Children – Working Memory (CABC-WM)

Published on: June 12, 2017

Area of Science:

Speech-language pathology
Computational linguistics
Developmental psychology

Background:

Nonword repetition (NWR) tests are crucial for identifying language impairments.
Manual scoring of NWR tests by speech-language pathologists is time-consuming and subjective.
Automated evaluation methods are needed to improve efficiency and objectivity.

Purpose of the Study:

To investigate the feasibility of using automated speech-based techniques to evaluate NWR tests.
To develop and validate a machine learning model for predicting NWR test scores.

Main Methods:

Utilized Automatic Speech Recognition (ASR) to transcribe children's verbal responses to nonword stimuli.
Applied machine learning algorithms to ASR output to predict gold-standard scores.
Evaluated the model on a dataset of 101 children, including those with autism spectrum disorders (ASD), specific language impairment (SLI), and typically developing (TD) children.

Main Results:

The proposed automated approach demonstrated significant success in predicting NWR test scores.
Achieved an averaged product-moment correlation of 0.74 between observed and predicted scores.
Reported a mean absolute error of 0.06 on scores ranging from 0.34 to 0.97.

Conclusions:

Automated speech-based evaluation of NWR tests is a feasible and effective approach.
This technology has the potential to assist clinicians in diagnosing language impairments more efficiently.
Further research can refine these methods for broader clinical application.