# A model of early word acquisition based on realistic-scale audiovisual naming events

arXiv: https://arxiv.org/abs/2406.05259 · HTML: https://mlsanity.com/paper/2406.05259/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 73 references that are in the map.
- First posted on arXiv 7 Jun 2024.

## Builds on (most cited first, 26 of 26)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Jointly Discovering Visual Objects and Spoken Words from Raw Sensory Input](https://mlsanity.com/paper/1804.01452/) (2018)
- [Cognitive Science in the era of Artificial Intelligence: A roadmap for reverse-engineering the infant language-learner](https://mlsanity.com/paper/1607.08723/) (2016)
- [Text-Free Image-to-Speech Synthesis Using Learned Segmental Units](https://mlsanity.com/paper/2012.15454/) (2020)
- [Learning Hierarchical Discrete Linguistic Units from Visually-Grounded Speech](https://mlsanity.com/paper/1911.09602/) (2019)
- [Representations of language in a model of visually grounded speech signal](https://mlsanity.com/paper/1702.01991/) (2017)
- [Word Discovery in Visually Grounded, Self-Supervised Speech Models](https://mlsanity.com/paper/2203.15081/) (2022)
- [Self-supervised language learning from raw audio: Lessons from the Zero Resource Speech Challenge](https://mlsanity.com/paper/2210.15759/) (2022)
- [Large-scale representation learning from visually grounded untranscribed speech](https://mlsanity.com/paper/1909.08782/) (2019)
- [Visually grounded models of spoken language: A survey of datasets, architectures and evaluation techniques](https://mlsanity.com/paper/2104.13225/) (2021)
- [BabySLM: language-acquisition-friendly benchmark of self-supervised spoken language models](https://mlsanity.com/paper/2306.01506/) (2023)
- [Encoding of phonology in a recurrent neural model of grounded speech](https://mlsanity.com/paper/1706.03815/) (2017)
- [Self-Supervised Representation Learning for Speech Using Visual Grounding and Masked Language Modeling](https://mlsanity.com/paper/2202.03543/) (2022)
- [Fast-Slow Transformer for Visually Grounding Speech](https://mlsanity.com/paper/2109.08186/) (2021)
- [Word Recognition, Competition, and Activation in a Model of Visually Grounded Speech](https://mlsanity.com/paper/1909.08491/) (2019)
- [Can phones, syllables, and words emerge as side-products of cross-situational audiovisual learning? -- A computational investigation](https://mlsanity.com/paper/2109.14200/) (2021)
- [Syllable Discovery and Cross-Lingual Generalization in a Visually Grounded, Self-Supervised Speech Model](https://mlsanity.com/paper/2305.11435/) (2023)
- [A computational model of early language acquisition from audiovisual experiences of young infants](https://mlsanity.com/paper/1906.09832/) (2019)
- [Evaluation of Audio-Visual Alignments in Visually Grounded Speech Models](https://mlsanity.com/paper/2108.02562/) (2021)
- [Learning English with Peppa Pig](https://mlsanity.com/paper/2202.12917/) (2022)
- [Simultaneous or Sequential Training? How Speech Representations Cooperate in a Multi-Task Self-Supervised Learning System](https://mlsanity.com/paper/2306.02972/) (2023)

## Built on by (most cited first, 3 of 3)

- [Computational modeling of early language learning from acoustic speech and audiovisual input without linguistic priors](https://mlsanity.com/paper/2603.08359/) (2026)
- [Do Blind Spots Matter for Word-Referent Mapping? A Computational Study with Infant Egocentric Video](https://mlsanity.com/paper/2511.11725/) (2025)
- [EgoBabyVLM: Benchmarking Cross-Modal Learning from Naturalistic Egocentric Video Data](https://mlsanity.com/paper/2605.19130/) (2026)

## Abstract (arXiv)

Infants gradually learn to parse continuous speech into words and connect names with objects, yet the mechanisms behind development of early word perception skills remain unknown. We studied the extent to which early words can be acquired through statistical learning from regularities in audiovisual sensory input. We simulated word learning in infants up to 12 months of age in a realistic setting, using a model that solely learns from statistical regularities in unannotated raw speech and pixel-level visual input. Crucially, the quantity of object naming events was carefully designed to match that accessible to infants of comparable ages. Results show that the model effectively learns to recognize words and associate them with corresponding visual objects, with a vocabulary growth rate comparable to that observed in infants. The findings support the viability of general statistical learning for early word perception, demonstrating how learning can operate without assuming any prior linguistic capabilities.