Automated Pronunciation Evaluation System Developed for Korean Toddler Speech
Researchers have created an automated system to evaluate pronunciation in Korean-speaking toddlers aged 2-5 years, combining speaker diarization and self-supervised learning models. The system addresses a significant gap in assessment tools, as speech sound disorders affect approximately 44% of Korean pediatric communication disorder cases. This development could improve early detection and intervention for speech disorders in Korean children.
A new end-to-end pipeline for automated pronunciation evaluation of Korean toddler speech has been presented, combining neural speaker diarization with self-supervised speech representation learning. The research introduces a novel IRB-approved corpus of 53 recordings from Korean-speaking children aged 2-5 years, with 1,190 consonant and 748 vowel word-level labels annotated by three independent reviewers. The NeMo SortFormer diarization model achieved 88.69% speaker count accuracy and 33.04% diarization error rate, effectively handling the acoustic challenge of distinguishing between toddler speech and caregivers using aegyo (baby talk). For pronunciation scoring, a cross-model ensemble routing consonant prediction to HuBERT-large and vowel prediction to WavLM-large achieved balanced accuracies of 0.720 and 0.845 respectively. This work addresses a critical gap in automated assessment tools for Korean toddler speech, where speech sound disorders are a major component of pediatric communication disorders.
What's missing
The study does not discuss clinical validation with speech-language pathologists, comparison with existing assessment methods, or potential deployment pathways for clinical use. Additionally, generalization to other Korean dialects or socioeconomic backgrounds is not addressed.
What different sources said
- arXiv cs.AICenter
Automated Pronunciation Evaluation for Korean Toddler Speech using Speech Diarization and Self-Supervised Learning
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