# Supervised online diarization with sample mean loss for multi-domain data

arXiv: https://arxiv.org/abs/1911.01266 · HTML: https://mlsanity.com/paper/1911.01266/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Nov 2019.

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

- [Online Neural Diarization of Unlimited Numbers of Speakers Using Global and Local Attractors](https://mlsanity.com/paper/2206.02432/) (2022)
- [Systematic Evaluation of Online Speaker Diarization Systems Regarding their Latency](https://mlsanity.com/paper/2407.04293/) (2024)
- [A Review of Common Online Speaker Diarization Methods](https://mlsanity.com/paper/2406.14464/) (2024)

## Abstract (arXiv)

Recently, a fully supervised speaker diarization approach was proposed (UIS-RNN) which models speakers using multiple instances of a parameter-sharing recurrent neural network. In this paper we propose qualitative modifications to the model that significantly improve the learning efficiency and the overall diarization performance. In particular, we introduce a novel loss function, we called Sample Mean Loss and we present a better modelling of the speaker turn behaviour, by devising an analytical expression to compute the probability of a new speaker joining the conversation. In addition, we demonstrate that our model can be trained on fixed-length speech segments, removing the need for speaker change information in inference. Using x-vectors as input features, we evaluate our proposed approach on the multi-domain dataset employed in the DIHARD II challenge: our online method improves with respect to the original UIS-RNN and achieves similar performance to an offline agglomerative clustering baseline using PLDA scoring.