# Robust End-to-End Diarization with Domain Adaptive Training and Multi-Task Learning

arXiv: https://arxiv.org/abs/2312.07136 · HTML: https://mlsanity.com/paper/2312.07136/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Dec 2023.

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

- [Speakers Unembedded: Embedding-free Approach to Long-form Neural Diarization](https://mlsanity.com/paper/2406.18679/) (2024)
- [Configurable Multilingual ASR with Speech Summary Representations](https://mlsanity.com/paper/2410.04478/) (2024)

## Abstract (arXiv)

Due to the scarcity of publicly available diarization data, the model performance can be improved by training a single model with data from different domains. In this work, we propose to incorporate domain information to train a single end-to-end diarization model for multiple domains. First, we employ domain adaptive training with parameter-efficient adapters for on-the-fly model reconfiguration. Second, we introduce an auxiliary domain classification task to make the diarization model more domain-aware. For seen domains, the combination of our proposed methods reduces the absolute DER from 17.66% to 16.59% when compared with the baseline. During inference, adapters from ground-truth domains are not available for unseen domains. We demonstrate our model exhibits a stronger generalizability to unseen domains when adapters are removed. For two unseen domains, this improves the DER performance from 39.91% to 23.09% and 25.32% to 18.76% over the baseline, respectively.