# Challenging margin-based speaker embedding extractors by using the variational information bottleneck

arXiv: https://arxiv.org/abs/2406.12622 · HTML: https://mlsanity.com/paper/2406.12622/

## Key facts

- No citing papers in the map yet (data through 19 Oct 2026).
- Placed in Speaker recognition and diarization, one of 11 directions in Speech and audio language models.
- Builds on 23 of its 35 references that are in the map.
- First posted on arXiv 18 Jun 2024.

## Placed in

- Speech and audio language models / [Speaker recognition and diarization](https://mlsanity.com/speech/speaker-recognition-and-diarization/)

## Builds on (most cited first, 23 of 23)

- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [VoxCeleb2: Deep Speaker Recognition](https://mlsanity.com/paper/1806.05622/) (2018)
- [ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification](https://mlsanity.com/paper/2005.07143/) (2020)
- [Deep Variational Information Bottleneck](https://mlsanity.com/paper/1612.00410/) (2016)
- [Wespeaker: A Research and Production oriented Speaker Embedding Learning Toolkit](https://mlsanity.com/paper/2210.17016/) (2022)
- [BUT System Description to VoxCeleb Speaker Recognition Challenge 2019](https://mlsanity.com/paper/1910.12592/) (2019)
- [Learning to Learn with Variational Information Bottleneck for Domain Generalization](https://mlsanity.com/paper/2007.07645/) (2020)
- [TitaNet: Neural Model for speaker representation with 1D Depth-wise separable convolutions and global context](https://mlsanity.com/paper/2110.04410/) (2021)
- [End-to-End Text-Dependent Speaker Verification](https://mlsanity.com/paper/1509.08062/) (2015)
- [CN-Celeb: multi-genre speaker recognition](https://mlsanity.com/paper/2012.12468/) (2020)
- [Margin Matters: Towards More Discriminative Deep Neural Network Embeddings for Speaker Recognition](https://mlsanity.com/paper/1906.07317/) (2019)
- [Explaining a black-box using Deep Variational Information Bottleneck Approach](https://mlsanity.com/paper/1902.06918/) (2019)
- [Disentangling Voice and Content with Self-Supervision for Speaker Recognition](https://mlsanity.com/paper/2310.01128/) (2023)
- [Xi-Vector Embedding for Speaker Recognition](https://mlsanity.com/paper/2108.05679/) (2021)
- [The 2021 NIST Speaker Recognition Evaluation](https://mlsanity.com/paper/2204.10242/) (2022)
- [Parameter-efficient transfer learning of pre-trained Transformer models for speaker verification using adapters](https://mlsanity.com/paper/2210.16032/) (2022)
- [NIST SRE CTS Superset: A large-scale dataset for telephony speaker recognition](https://mlsanity.com/paper/2108.07118/) (2021)
- [Anti-Spoofing Using Transfer Learning with Variational Information Bottleneck](https://mlsanity.com/paper/2204.01387/) (2022)
- [Robust Speaker Recognition with Transformers Using wav2vec 2.0](https://mlsanity.com/paper/2203.15095/) (2022)
- [Enhancing Multimodal Entity and Relation Extraction with Variational Information Bottleneck](https://mlsanity.com/paper/2304.02328/) (2023)
- [Incorporating Uncertainty from Speaker Embedding Estimation to Speaker Verification](https://mlsanity.com/paper/2302.11763/) (2023)
- [Analyzing speaker verification embedding extractors and back-ends under language and channel mismatch](https://mlsanity.com/paper/2203.10300/) (2022)
- [Toroidal Probabilistic Spherical Discriminant Analysis](https://mlsanity.com/paper/2210.15441/) (2022)

## Abstract (arXiv)

Speaker embedding extractors are typically trained using a classification loss over the training speakers. During the last few years, the standard softmax/cross-entropy loss has been replaced by the margin-based losses, yielding significant improvements in speaker recognition accuracy. Motivated by the fact that the margin merely reduces the logit of the target speaker during training, we consider a probabilistic framework that has a similar effect. The variational information bottleneck provides a principled mechanism for making deterministic nodes stochastic, resulting in an implicit reduction of the posterior of the target speaker. We experiment with a wide range of speaker recognition benchmarks and scoring methods and report competitive results to those obtained with the state-of-the-art Additive Angular Margin loss.