# Simple Attention Module based Speaker Verification with Iterative noisy label detection

arXiv: https://arxiv.org/abs/2110.06534 · HTML: https://mlsanity.com/paper/2110.06534/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Oct 2021.

## Built on by (most cited first, 14 of 14)

- [The VoxCeleb Speaker Recognition Challenge: A Retrospective](https://mlsanity.com/paper/2408.14886/) (2024)
- [Learning Audio-Visual embedding for Person Verification in the Wild](https://mlsanity.com/paper/2209.04093/) (2022)
- [Scores Know Bobs Voice: Speaker Impersonation Attack](https://mlsanity.com/paper/2603.02781/) (2026)
- [A Teacher-Student approach for extracting informative speaker embeddings from speech mixtures](https://mlsanity.com/paper/2306.00634/) (2023)
- [Toward Improving Synthetic Audio Spoofing Detection Robustness via Meta-Learning and Disentangled Training With Adversarial Examples](https://mlsanity.com/paper/2408.13341/) (2024)
- [UniPET-SPK: A Unified Framework for Parameter-Efficient Tuning of Pre-trained Speech Models for Robust Speaker Verification](https://mlsanity.com/paper/2501.16542/) (2025)
- [ChinaTelecom System Description to VoxCeleb Speaker Recognition Challenge 2023](https://mlsanity.com/paper/2308.08181/) (2023)
- [More than Vanilla Fusion: a Simple, Decoupling-free, Attention Module for Multimodal Fusion Based on Signal Theory](https://mlsanity.com/paper/2312.07212/) (2023)
- [Memory-Efficient Training for Deep Speaker Embedding Learning in Speaker Verification](https://mlsanity.com/paper/2412.01195/) (2024)
- [Comparison of End-to-end Speech Assessment Models for the NOCASA 2025 Challenge](https://mlsanity.com/paper/2509.03256/) (2025)
- [The First Voice Timbre Attribute Detection Challenge](https://mlsanity.com/paper/2509.06635/) (2025)
- [Triage knowledge distillation for speaker verification](https://mlsanity.com/paper/2601.14699/) (2026)
- [L-Proto: Language-Aware Episodic Prototypical Training for Multilingual Speaker Verification](https://mlsanity.com/paper/2606.17416/) (2026)
- [Simple Language Normalization Wins: Cross-Lingual Speaker Verification for the TidyVoice 2026 Challenge](https://mlsanity.com/paper/2607.22923/) (2026)

## Abstract (arXiv)

Recently, the attention mechanism such as squeeze-and-excitation module (SE) and convolutional block attention module (CBAM) has achieved great success in deep learning-based speaker verification system. This paper introduces an alternative effective yet simple one, i.e., simple attention module (SimAM), for speaker verification. The SimAM module is a plug-and-play module without extra modal parameters. In addition, we propose a noisy label detection method to iteratively filter out the data samples with a noisy label from the training data, considering that a large-scale dataset labeled with human annotation or other automated processes may contain noisy labels. Data with the noisy label may over parameterize a deep neural network (DNN) and result in a performance drop due to the memorization effect of the DNN. Experiments are conducted on VoxCeleb dataset. The speaker verification model with SimAM achieves the 0.675% equal error rate (EER) on VoxCeleb1 original test trials. Our proposed iterative noisy label detection method further reduces the EER to 0.643%.