Usefulness of Automatic Speech Recognition Assessment of Children With Speech Sound Disorders: Validation Study.

ABSTRACT:

Background

Speech sound disorders (SSDs) are common communication challenges in children, typically assessed by speech-language pathologists (SLPs) using standardized tools. However, traditional evaluation methods are time-intensive and prone to variability, raising concerns about reliability.

Objective

This study aimed to compare the evaluation outcomes of SLPs and an automatic speech recognition (ASR) model using two standardized SSD assessments in South Korea, evaluating the ASR model's performance.

Methods

A fine-tuned wav2vec 2.0 XLS-R model, pretrained on 436,000 hours of adult voice data spanning 128 languages, was used. The model was further trained on 93.6 minutes of children's voices with articulation errors to improve error detection. Participants included

SUBMITTER: Kim DH 

PROVIDER: S-EPMC11775490 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature