Publications by authors named "Younggon Im"
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.
Article Synopsis
- The study develops an automatic speech recognition (ASR) model specifically to diagnose pronunciation problems in children with speech sound disorders (SSDs), aiming to replace manual transcription methods.
- The researchers fine-tuned the wav2vec2.0 XLS-R model to better recognize the way children with SSDs pronounce words, achieving a Phoneme Error Rate (PER) of only 10%.
- In comparison, a leading ASR model called Whisper struggled with this task, showing a much higher PER of about 50%, highlighting the need for more specialized ASR approaches in clinical settings.