Accuracy of Speech Sound Analysis: Comparison of an Automatic Artificial Intelligence Algorithm With Clinician Assessment.

Journal: Journal of speech, language, and hearing research : JSLHR

PMID: 39173066

Abstract

PURPOSE: Automatic speech analysis (ASA) and automatic speech recognition systems are increasingly being used in the treatment of speech sound disorders (SSDs). When utilized as a home practice tool or in the absence of the clinician, the ASA system has the potential to facilitate treatment gains. However, the feedback accuracy of such systems varies, a factor that may impact these gains. The current research analyzes the feedback accuracy of a novel ASA algorithm (Amplio Learning Technologies), in comparison to clinician judgments.

Authors

Micalle Carl

Department of Communication Disorders, Ariel University, Israel.
Eduard Rudyk

Amplio Learning Technologies, Rockville, MD.
Yair Shapira

Amplio Learning Technologies, Rockville, MD.
Heather Leavy Rusiewicz

Department of Speech-Language Pathology, Duquesne University, Pittsburgh, PA.
Michal Icht

Department of Communication Disorders, Ariel University, Israel.

Keywords

Adolescent Algorithms Artificial Intelligence Child Child, Preschool Female Humans Male Phonetics Reproducibility of Results Speech Production Measurement Speech Recognition Software Speech Sound Disorder

External Resources

View on PubMed Access via DOI PubMed (39173066)

Accuracy of Speech Sound Analysis: Comparison of an Automatic Artificial Intelligence Algorithm With Clinician Assessment.

Abstract

Authors

Keywords

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