# Robust Self Supervised Speech Embeddings for Child-Adult Classification in Interactions involving Children with Autism

arXiv: https://arxiv.org/abs/2307.16398 · HTML: https://mlsanity.com/paper/2307.16398/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jul 2023.

## Built on by (most cited first, 4 of 4)

- [Exploring Speech Foundation Models for Speaker Diarization in Child-Adult Dyadic Interactions](https://mlsanity.com/paper/2406.07890/) (2024)
- [Children's Speech Recognition through Discrete Token Enhancement](https://mlsanity.com/paper/2406.13431/) (2024)
- [Egocentric Speaker Classification in Child-Adult Dyadic Interactions: From Sensing to Computational Modeling](https://mlsanity.com/paper/2409.09340/) (2024)
- [Robust Multi-Tier Infant-Centered Audio Understanding with Whisper via Structured Speaker Conditioning](https://mlsanity.com/paper/2608.11587/) (2026)

## Abstract (arXiv)

We address the problem of detecting who spoke when in child-inclusive spoken interactions i.e., automatic child-adult speaker classification. Interactions involving children are richly heterogeneous due to developmental differences. The presence of neurodiversity e.g., due to Autism, contributes additional variability. We investigate the impact of additional pre-training with more unlabelled child speech on the child-adult classification performance. We pre-train our model with child-inclusive interactions, following two recent self-supervision algorithms, Wav2vec 2.0 and WavLM, with a contrastive loss objective. We report 9 - 13% relative improvement over the state-of-the-art baseline with regards to classification F1 scores on two clinical interaction datasets involving children with Autism. We also analyze the impact of pre-training under different conditions by evaluating our model on interactions involving different subgroups of children based on various demographic factors.