# End-to-End Speaker Diarization Conditioned on Speech Activity and Overlap Detection

arXiv: https://arxiv.org/abs/2106.04078 · HTML: https://mlsanity.com/paper/2106.04078/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2021.

## Built on by (most cited first, 5 of 5)

- [Towards Neural Diarization for Unlimited Numbers of Speakers Using Global and Local Attractors](https://mlsanity.com/paper/2107.01545/) (2021)
- [Sortformer: A Novel Approach for Permutation-Resolved Speaker Supervision in Speech-to-Text Systems](https://mlsanity.com/paper/2409.06656/) (2024)
- [Online Neural Diarization of Unlimited Numbers of Speakers Using Global and Local Attractors](https://mlsanity.com/paper/2206.02432/) (2022)
- [Joint Speech Activity and Overlap Detection with Multi-Exit Architecture](https://mlsanity.com/paper/2209.11906/) (2022)
- [Conversational Rubert for Detecting Competitive Interruptions in ASR-Transcribed Dialogues](https://mlsanity.com/paper/2407.14940/) (2024)

## Abstract (arXiv)

In this paper, we present a conditional multitask learning method for end-to-end neural speaker diarization (EEND). The EEND system has shown promising performance compared with traditional clustering-based methods, especially in the case of overlapping speech. In this paper, to further improve the performance of the EEND system, we propose a novel multitask learning framework that solves speaker diarization and a desired subtask while explicitly considering the task dependency. We optimize speaker diarization conditioned on speech activity and overlap detection that are subtasks of speaker diarization, based on the probabilistic chain rule. Experimental results show that our proposed method can leverage a subtask to effectively model speaker diarization, and outperforms conventional EEND systems in terms of diarization error rate.