# Leveraging Speaker Embeddings in End-to-End Neural Diarization for Two-Speaker Scenarios

arXiv: https://arxiv.org/abs/2407.01317 · HTML: https://mlsanity.com/paper/2407.01317/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 28 of its 46 references that are in the map.
- First posted on arXiv 1 Jul 2024.

## Builds on (most cited first, 28 of 28)

- [VoxCeleb2: Deep Speaker Recognition](https://mlsanity.com/paper/1806.05622/) (2018)
- [VoxCeleb: a large-scale speaker identification dataset](https://mlsanity.com/paper/1706.08612/) (2017)
- [MUSAN: A Music, Speech, and Noise Corpus](https://mlsanity.com/paper/1510.08484/) (2015)
- [Generalized End-to-End Loss for Speaker Verification](https://mlsanity.com/paper/1710.10467/) (2017)
- [Permutation Invariant Training of Deep Models for Speaker-Independent Multi-talker Speech Separation](https://mlsanity.com/paper/1607.00325/) (2016)
- [A Review of Speaker Diarization: Recent Advances with Deep Learning](https://mlsanity.com/paper/2101.09624/) (2021)
- [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)
- [Bayesian HMM clustering of x-vector sequences (VBx) in speaker diarization: theory, implementation and analysis on standard tasks](https://mlsanity.com/paper/2012.14952/) (2020)
- [End-to-End Speaker Diarization for an Unknown Number of Speakers with Encoder-Decoder Based Attractors](https://mlsanity.com/paper/2005.09921/) (2020)
- [Target-Speaker Voice Activity Detection: a Novel Approach for Multi-Speaker Diarization in a Dinner Party Scenario](https://mlsanity.com/paper/2005.07272/) (2020)
- [ECAPA-TDNN Embeddings for Speaker Diarization](https://mlsanity.com/paper/2104.01466/) (2021)
- [Integrating end-to-end neural and clustering-based diarization: Getting the best of both worlds](https://mlsanity.com/paper/2010.13366/) (2020)
- [Turn-to-Diarize: Online Speaker Diarization Constrained by Transformer Transducer Speaker Turn Detection](https://mlsanity.com/paper/2109.11641/) (2021)
- [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)
- [Probing the Information Encoded in X-vectors](https://mlsanity.com/paper/1909.06351/) (2019)
- [End-to-End Neural Diarization: Reformulating Speaker Diarization as Simple Multi-label Classification](https://mlsanity.com/paper/2003.02966/) (2020)
- [End-to-end Neural Diarization: From Transformer to Conformer](https://mlsanity.com/paper/2106.07167/) (2021)
- [EEND-SS: Joint End-to-End Neural Speaker Diarization and Speech Separation for Flexible Number of Speakers](https://mlsanity.com/paper/2203.17068/) (2022)
- [Augmenting Transformer-Transducer Based Speaker Change Detection With Token-Level Training Loss](https://mlsanity.com/paper/2211.06482/) (2022)
- [Multi-scale speaker embedding-based graph attention networks for speaker diarisation](https://mlsanity.com/paper/2110.03361/) (2021)
- [Target Speaker Voice Activity Detection with Transformers and Its Integration with End-to-End Neural Diarization](https://mlsanity.com/paper/2208.13085/) (2022)
- [Attention-based Encoder-Decoder Network for End-to-End Neural Speaker Diarization with Target Speaker Attractor](https://mlsanity.com/paper/2305.10704/) (2023)
- [An Experimental Review of Speaker Diarization methods with application to Two-Speaker Conversational Telephone Speech recordings](https://mlsanity.com/paper/2305.18074/) (2023)
- [Frame-wise and overlap-robust speaker embeddings for meeting diarization](https://mlsanity.com/paper/2306.00625/) (2023)
- [Improving Transformer-based End-to-End Speaker Diarization by Assigning Auxiliary Losses to Attention Heads](https://mlsanity.com/paper/2303.01192/) (2023)

## Abstract (arXiv)

End-to-end neural speaker diarization systems are able to address the speaker diarization task while effectively handling speech overlap. This work explores the incorporation of speaker information embeddings into the end-to-end systems to enhance the speaker discriminative capabilities, while maintaining their overlap handling strengths. To achieve this, we propose several methods for incorporating these embeddings along the acoustic features. Furthermore, we delve into an analysis of the correct handling of silence frames, the window length for extracting speaker embeddings and the transformer encoder size. The effectiveness of our proposed approach is thoroughly evaluated on the CallHome dataset for the two-speaker diarization task, with results that demonstrate a significant reduction in diarization error rates achieving a relative improvement of a 10.78% compared to the baseline end-to-end model.