Papers › Data Efficient Child-Adult Speaker Diarization with Simulated Conversations

Data Efficient Child-Adult Speaker Diarization with Simulated Conversations

13 Sep 2024arXiv:2409.08881archive 2025-07-28

Anfeng Xu, Tiantian Feng, Helen Tager-Flusberg, Catherine Lord, Shrikanth Narayanan

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.

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Speaker DiarizationTransfer Learningspeaker-diarization

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