# Data Efficient Child-Adult Speaker Diarization with Simulated Conversations

arXiv: https://arxiv.org/abs/2409.08881 · HTML: https://mlsanity.com/paper/2409.08881/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Sep 2024.

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

- [Can Generic LLMs Help Analyze Child-adult Interactions Involving Children with Autism in Clinical Observation?](https://mlsanity.com/paper/2411.10761/) (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)

Automating child speech analysis is crucial for applications such as neurocognitive assessments. Speaker diarization, which identifies ``who spoke when'', is an essential component of the automated analysis. However, publicly available child-adult speaker diarization solutions are scarce due to privacy concerns and a lack of annotated datasets, while manually annotating data for each scenario is both time-consuming and costly. To overcome these challenges, we propose a data-efficient solution by creating simulated child-adult conversations using AudioSet. We then train a Whisper Encoder-based model, achieving strong zero-shot performance on child-adult speaker diarization using real datasets. The model performance improves substantially when fine-tuned with only 30 minutes of real train data, with LoRA further improving the transfer learning performance. The source code and the child-adult speaker diarization model trained on simulated conversations are publicly available.