# Understanding Spoken Language Development of Children with ASD Using Pre-trained Speech Embeddings

arXiv: https://arxiv.org/abs/2305.14117 · HTML: https://mlsanity.com/paper/2305.14117/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 30 references that are in the map.
- First posted on arXiv 23 May 2023.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [SUPERB: Speech processing Universal PERformance Benchmark](https://mlsanity.com/paper/2105.01051/) (2021)
- [A Review of Speech-centric Trustworthy Machine Learning: Privacy, Safety, and Fairness](https://mlsanity.com/paper/2212.09006/) (2022)
- [Meta-learning for robust child-adult classification from speech](https://mlsanity.com/paper/1910.11400/) (2019)
- [TrustSER: On the Trustworthiness of Fine-tuning Pre-trained Speech Embeddings For Speech Emotion Recognition](https://mlsanity.com/paper/2305.11229/) (2023)
- [Learning Domain Invariant Representations for Child-Adult Classification from Speech](https://mlsanity.com/paper/1910.11472/) (2019)

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

- [Joint Audio and Speech Understanding](https://mlsanity.com/paper/2309.14405/) (2023)
- [Exploring Speech Foundation Models for Speaker Diarization in Child-Adult Dyadic Interactions](https://mlsanity.com/paper/2406.07890/) (2024)
- [Egocentric Speaker Classification in Child-Adult Dyadic Interactions: From Sensing to Computational Modeling](https://mlsanity.com/paper/2409.09340/) (2024)

## Abstract (arXiv)

Speech processing techniques are useful for analyzing speech and language development in children with Autism Spectrum Disorder (ASD), who are often varied and delayed in acquiring these skills. Early identification and intervention are crucial, but traditional assessment methodologies such as caregiver reports are not adequate for the requisite behavioral phenotyping. Natural Language Sample (NLS) analysis has gained attention as a promising complement. Researchers have developed benchmarks for spoken language capabilities in children with ASD, obtainable through the analysis of NLS. This paper proposes applications of speech processing technologies in support of automated assessment of children's spoken language development by classification between child and adult speech and between speech and nonverbal vocalization in NLS, with respective F1 macro scores of 82.6% and 67.8%, underscoring the potential for accurate and scalable tools for ASD research and clinical use.