Automated Pronunciation Evaluation for Korean Toddler Speech using Speech Diarization and Self-Supervised Learning
arXiv:2606.10213v1 Announce Type: cross Abstract: Speech sound disorders affect approximately 44% of Korean pediatric communication disorder cases, yet automated assessment tools for Korean toddler speech remain underdeveloped. This paper presents an end-to-end pipeline for automated pronunciation evaluation of Korean toddler speech, combining neural speaker diarization with self-supervised speech representation learning. We introduce a novel IRB-approved corpus of 53 recordings from Korean-speaking children aged 2-5 years. A subset of 53 subjects was annotated by three independent reviewers,
The increasing maturity of AI, particularly in speech processing and self-supervised learning, enables the development of specialized applications for previously underserved linguistic and demographic groups.
This development indicates advancements in AI for specialized medical/developmental applications and potentially broader accessibility for non-dominant languages, addressing critical public health needs.
The ability to accurately assess pronunciation in specific populations like Korean toddlers moves from manual, subjective methods to automated, data-driven approaches, improving diagnostic capabilities.
- · AI developers (speech technology)
- · Healthcare providers (pediatrics, speech therapy)
- · Korean-speaking children with speech disorders
- · Parents of children with speech disorders
- · Manual speech evaluation services (for basic screening)
Automated tools will become more prevalent for early detection and intervention of speech sound disorders in diverse linguistic contexts.
The data collected from such tools could inform large-scale studies on language acquisition and speech pathologies across different cultures.
This could lead to personalized AI tutors or therapeutic AI agents specifically designed to assist with language development issues in early childhood.
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Read at arXiv cs.AI