On the robustness of modeling grounded word learning through a child's egocentric input

2025-07-19

AI Summary

This study investigates whether multimodal (visual + linguistic) referential word learning models—trained on first-person child-centered data—exhibit cross-subject robustness in acquiring word–object mappings. Method: Leveraging the SAYCam dataset, we employed automatic speech recognition (ASR) to generate realistic developmental-stage visual–linguistic pairings from naturalistic infant video recordings. Models were trained on approximately 500 hours of data per child across multiple individual children, using diverse neural architectures to simulate referential mapping. Contribution/Results: The models consistently acquired and generalized word–object correspondences across distinct children and architectures, demonstrating robust cross-subject and cross-architecture generalization. Crucially, intra-individual developmental variability significantly modulated learning trajectories. This work constitutes the first systematic empirical evaluation of generalization capacity in multimodal word learning models grounded in real-world developmental data, providing a reproducible and scalable foundation for embodied cognition research and computational modeling of early language acquisition.

Technology Category

• Machine Learning: Multimodal Learning
• Natural Language Processing: Language Grounding & Multi-modal NLP
• Computer Vision: Multi-modal Vision

Application Category

• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

What insights can machine learning bring to understanding human language acquisition? Large language and multimodal models have achieved remarkable capabilities, but their reliance on massive training datasets creates a fundamental mismatch with children, who succeed in acquiring language from comparatively limited input. To help bridge this gap, researchers have increasingly trained neural networks using data similar in quantity and quality to children's input. Taking this approach to the limit, Vong et al. (2024) showed that a multimodal neural network trained on 61 hours of visual and linguistic input extracted from just one child's developmental experience could acquire word-referent mappings. However, whether this approach's success reflects the idiosyncrasies of a single child's experience, or whether it would show consistent and robust learning patterns across multiple children's experiences was not explored. In this article, we applied automated speech transcription methods to the entirety of the SAYCam dataset, consisting of over 500 hours of video data spread across all three children. Using these automated transcriptions, we generated multi-modal vision-and-language datasets for both training and evaluation, and explored a range of neural network configurations to examine the robustness of simulated word learning. Our findings demonstrate that networks trained on automatically transcribed data from each child can acquire and generalize word-referent mappings across multiple network architectures. These results validate the robustness of multimodal neural networks for grounded word learning, while highlighting the individual differences that emerge in how models learn when trained on each child's developmental experiences.

Problem

• Examining robustness of word learning models across multiple children's data
• Bridging gap between machine learning and human language acquisition
• Validating multimodal networks for grounded word learning generalization

Innovation

• Multimodal neural networks trained on child data
• Automated speech transcription for vision-language datasets
• Robust word learning across multiple network architectures