AUTHOR=Nishio Monami , Koyanagi Ayuha , Yakura Hiromu , Hanawa Takaya , Shi Shoi 

TITLE=Language-specific development of noun bias beyond infancy

JOURNAL=Frontiers in Language Sciences

VOLUME=Volume 4 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/language-sciences/articles/10.3389/flang.2025.1556481

DOI=10.3389/flang.2025.1556481

ISSN=2813-4605

ABSTRACT=Speech and language delays can significantly impact a child's learning, literacy, and social development, making early detection—particularly through vocabulary monitoring—essential. One well-established phenomenon in early language acquisition is the “noun bias,” where infants acquire nouns more readily than verbs. However, the developmental trajectory of this bias beyond infancy remains unclear, especially across different languages. In this study, we analyzed spontaneous speech using AI-based voice analysis to examine vocabulary development in Japanese- and English-speaking children across a broad age range. We quantified changes in noun and verb use over time and found that noun growth plateaued earlier in English than in Japanese, resulting in a more pronounced and persistent noun bias in Japanese beyond infancy. These findings suggest that the early noun bias may gradually converge with adult-like noun-to-verb ratios, which differ substantially across languages (e.g., 23,800:7,921 in English vs. 71,460:7,886 in Japanese). This study demonstrates the utility of AI-based tools in advancing language development research and underscores their potential for clinical applications in identifying and assessing speech and language delays.