# Siamese Capsule Network for End-to-End Speaker Recognition In The Wild

arXiv: https://arxiv.org/abs/2009.13480 · HTML: https://mlsanity.com/paper/2009.13480/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Sep 2020.

## Built on by (most cited first, 6 of 6)

- [Self-Supervised Speech Representation Learning: A Review](https://mlsanity.com/paper/2205.10643/) (2022)
- [A Fine-tuned Wav2vec 2.0/HuBERT Benchmark For Speech Emotion Recognition, Speaker Verification and Spoken Language Understanding](https://mlsanity.com/paper/2111.02735/) (2021)
- [Audio Representation Learning by Distilling Video as Privileged Information](https://mlsanity.com/paper/2302.02845/) (2023)
- [A Study on Bias and Fairness In Deep Speaker Recognition](https://mlsanity.com/paper/2303.08026/) (2023)
- [Best Practices for Noise-Based Augmentation to Improve the Performance of Deployable Speech-Based Emotion Recognition Systems](https://mlsanity.com/paper/2104.08806/) (2021)
- [DSNet: Disentangled Siamese Network with Neutral Calibration for Speech Emotion Recognition](https://mlsanity.com/paper/2312.15593/) (2023)

## Abstract (arXiv)

We propose an end-to-end deep model for speaker verification in the wild. Our model uses thin-ResNet for extracting speaker embeddings from utterances and a Siamese capsule network and dynamic routing as the Back-end to calculate a similarity score between the embeddings. We conduct a series of experiments and comparisons on our model to state-of-the-art solutions, showing that our model outperforms all the other models using substantially less amount of training data. We also perform additional experiments to study the impact of different speaker embeddings on the Siamese capsule network. We show that the best performance is achieved by using embeddings obtained directly from the feature aggregation module of the Front-end and passing them to higher capsules using dynamic routing.