# Employing self-supervised learning models for cross-linguistic child speech maturity classification

arXiv: https://arxiv.org/abs/2506.08999 · HTML: https://mlsanity.com/paper/2506.08999/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 29 references that are in the map.
- First posted on arXiv 10 Jun 2025.

## Builds on (most cited first, 6 of 6)

- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Towards Better Domain Adaptation for Self-supervised Models: A Case Study of Child ASR](https://mlsanity.com/paper/2305.00115/) (2023)
- [An open-source voice type classifier for child-centered daylong recordings](https://mlsanity.com/paper/2005.12656/) (2020)
- [Enhancing Child Vocalization Classification with Phonetically-Tuned Embeddings for Assisting Autism Diagnosis](https://mlsanity.com/paper/2309.07287/) (2023)
- [Self-supervised learning for infant cry analysis](https://mlsanity.com/paper/2305.01578/) (2023)
- [Automatic Detection of Speech Sound Disorder in Child Speech Using Posterior-based Speaker Representations](https://mlsanity.com/paper/2203.15405/) (2022)

## Built on by (most cited first, 2 of 2)

- [BabyHuBERT: Multilingual Self-Supervised Learning for Segmenting Speakers in Child-Centered Long-Form Recordings](https://mlsanity.com/paper/2509.15001/) (2025)
- [Deriving Benchmarking Datasets from Long-Form Recordings: Challenges and Opportunities](https://mlsanity.com/paper/2607.03201/) (2026)

## Abstract (arXiv)

Speech technology systems struggle with many downstream tasks for child speech due to small training corpora and the difficulties that child speech pose. We apply a novel dataset, SpeechMaturity, to state-of-the-art transformer models to address a fundamental classification task: identifying child vocalizations. Unlike previous corpora, our dataset captures maximally ecologically-valid child vocalizations across an unprecedented sample, comprising children acquiring 25+ languages in the U.S., Bolivia, Vanuatu, Papua New Guinea, Solomon Islands, and France. The dataset contains 242,004 labeled vocalizations, magnitudes larger than previous work. Models were trained to distinguish between cry, laughter, mature (consonant+vowel), and immature speech (just consonant or vowel). Models trained on the dataset outperform state-of-the-art models trained on previous datasets, achieved classification accuracy comparable to humans, and were robust across rural and urban settings.