# Evaluating computational models of infant phonetic learning across languages

arXiv: https://arxiv.org/abs/2008.02888 · HTML: https://mlsanity.com/paper/2008.02888/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Aug 2020.

## Built on by (most cited first, 6 of 6)

- [Do self-supervised speech models develop human-like perception biases?](https://mlsanity.com/paper/2205.15819/) (2022)
- [Do Acoustic Word Embeddings Capture Phonological Similarity? An Empirical Study](https://mlsanity.com/paper/2106.08686/) (2021)
- [How Familiar Does That Sound? Cross-Lingual Representational Similarity Analysis of Acoustic Word Embeddings](https://mlsanity.com/paper/2109.10179/) (2021)
- [A phonetic model of non-native spoken word processing](https://mlsanity.com/paper/2101.11332/) (2021)
- [Integrating Form and Meaning: A Multi-Task Learning Model for Acoustic Word Embeddings](https://mlsanity.com/paper/2209.06633/) (2022)
- [Computational modeling of early language learning from acoustic speech and audiovisual input without linguistic priors](https://mlsanity.com/paper/2603.08359/) (2026)

## Abstract (arXiv)

In the first year of life, infants' speech perception becomes attuned to the sounds of their native language. Many accounts of this early phonetic learning exist, but computational models predicting the attunement patterns observed in infants from the speech input they hear have been lacking. A recent study presented the first such model, drawing on algorithms proposed for unsupervised learning from naturalistic speech, and tested it on a single phone contrast. Here we study five such algorithms, selected for their potential cognitive relevance. We simulate phonetic learning with each algorithm and perform tests on three phone contrasts from different languages, comparing the results to infants' discrimination patterns. The five models display varying degrees of agreement with empirical observations, showing that our approach can help decide between candidate mechanisms for early phonetic learning, and providing insight into which aspects of the models are critical for capturing infants' perceptual development.