# Improving Neural Diarization through Speaker Attribute Attractors and Local Dependency Modeling

arXiv: https://arxiv.org/abs/2506.05593 · HTML: https://mlsanity.com/paper/2506.05593/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 24 references that are in the map.
- First posted on arXiv 5 Jun 2025.

## Builds on (most cited first, 14 of 14)

- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [MUSAN: A Music, Speech, and Noise Corpus](https://mlsanity.com/paper/1510.08484/) (2015)
- [Permutation Invariant Training of Deep Models for Speaker-Independent Multi-talker Speech Separation](https://mlsanity.com/paper/1607.00325/) (2016)
- [End-to-End Neural Speaker Diarization with Permutation-Free Objectives](https://mlsanity.com/paper/1909.05952/) (2019)
- [End-to-End Neural Speaker Diarization with Self-attention](https://mlsanity.com/paper/1909.06247/) (2019)
- [Speaker Diarization with LSTM](https://mlsanity.com/paper/1710.10468/) (2017)
- [Deep attractor network for single-microphone speaker separation](https://mlsanity.com/paper/1611.08930/) (2016)
- [Encoder-Decoder Based Attractors for End-to-End Neural Diarization](https://mlsanity.com/paper/2106.10654/) (2021)
- [From Simulated Mixtures to Simulated Conversations as Training Data for End-to-End Neural Diarization](https://mlsanity.com/paper/2204.00890/) (2022)
- [Speaker-independent Speech Separation with Deep Attractor Network](https://mlsanity.com/paper/1707.03634/) (2017)
- [Recursive speech separation for unknown number of speakers](https://mlsanity.com/paper/1904.03065/) (2019)
- [End-to-End Neural Diarization: Reformulating Speaker Diarization as Simple Multi-label Classification](https://mlsanity.com/paper/2003.02966/) (2020)
- [Guided Source Separation Meets a Strong ASR Backend: Hitachi/Paderborn University Joint Investigation for Dinner Party ASR](https://mlsanity.com/paper/1905.12230/) (2019)
- [Neural Diarization with Non-autoregressive Intermediate Attractors](https://mlsanity.com/paper/2303.06806/) (2023)

## Abstract (arXiv)

In recent years, end-to-end approaches have made notable progress in addressing the challenge of speaker diarization, which involves segmenting and identifying speakers in multi-talker recordings. One such approach, Encoder-Decoder Attractors (EDA), has been proposed to handle variable speaker counts as well as better guide the network during training. In this study, we extend the attractor paradigm by moving beyond direct speaker modeling and instead focus on representing more detailed `speaker attributes' through a multi-stage process of intermediate representations. Additionally, we enhance the architecture by replacing transformers with conformers, a convolution-augmented transformer, to model local dependencies. Experiments demonstrate improved diarization performance on the CALLHOME dataset.