# MFA-Conformer: Multi-scale Feature Aggregation Conformer for Automatic Speaker Verification

arXiv: https://arxiv.org/abs/2203.15249 · HTML: https://mlsanity.com/paper/2203.15249/

## Key facts

- Cited by 47 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Mar 2022.

## Built on by (most cited first, 30 of 47)

- [ASVspoof 5: Crowdsourced Speech Data, Deepfakes, and Adversarial Attacks at Scale](https://mlsanity.com/paper/2408.08739/) (2024)
- [ESPnet-SPK: full pipeline speaker embedding toolkit with reproducible recipes, self-supervised front-ends, and off-the-shelf models](https://mlsanity.com/paper/2401.17230/) (2024)
- [Towards single integrated spoofing-aware speaker verification embeddings](https://mlsanity.com/paper/2305.19051/) (2023)
- [Leveraging ASR Pretrained Conformers for Speaker Verification through Transfer Learning and Knowledge Distillation](https://mlsanity.com/paper/2309.03019/) (2023)
- [a-DCF: an architecture agnostic metric with application to spoofing-robust speaker verification](https://mlsanity.com/paper/2403.01355/) (2024)
- [SVeritas: Benchmark for Robust Speaker Verification under Diverse Conditions](https://mlsanity.com/paper/2509.17091/) (2025)
- [In search of strong embedding extractors for speaker diarisation](https://mlsanity.com/paper/2210.14682/) (2022)
- [Unveiling Audio Deepfake Origins: A Deep Metric learning And Conformer Network Approach With Ensemble Fusion](https://mlsanity.com/paper/2506.02085/) (2025)
- [CoLMbo: Speaker Language Model for Descriptive Profiling](https://mlsanity.com/paper/2506.09375/) (2025)
- [Large-scale learning of generalised representations for speaker recognition](https://mlsanity.com/paper/2210.10985/) (2022)
- [Introducing Model Inversion Attacks on Automatic Speaker Recognition](https://mlsanity.com/paper/2301.03206/) (2023)
- [HiddenSpeaker: Generate Imperceptible Unlearnable Audios for Speaker Verification System](https://mlsanity.com/paper/2405.15655/) (2024)
- [Improving Transformer-based Networks With Locality For Automatic Speaker Verification](https://mlsanity.com/paper/2302.08639/) (2023)
- [Cross-modal Audio-visual Co-learning for Text-independent Speaker Verification](https://mlsanity.com/paper/2302.11254/) (2023)
- [t-EER: Parameter-Free Tandem Evaluation of Countermeasures and Biometric Comparators](https://mlsanity.com/paper/2309.12237/) (2023)
- [Efficient Adapter Tuning of Pre-trained Speech Models for Automatic Speaker Verification](https://mlsanity.com/paper/2403.00293/) (2024)
- [PDAF: A Phonetic Debiasing Attention Framework For Speaker Verification](https://mlsanity.com/paper/2409.05799/) (2024)
- [Channel-Aware Domain-Adaptive Generative Adversarial Network for Robust Speech Recognition](https://mlsanity.com/paper/2409.12386/) (2024)
- [Enhancing Speaker Verification with w2v-BERT 2.0 and Knowledge Distillation guided Structured Pruning](https://mlsanity.com/paper/2510.04213/) (2025)
- [Adversarial Speaker Distillation for Countermeasure Model on Automatic Speaker Verification](https://mlsanity.com/paper/2203.17031/) (2022)
- [Voice Morphing: Two Identities in One Voice](https://mlsanity.com/paper/2309.02404/) (2023)
- [CAARMA: Class Augmentation with Adversarial Mixup Regularization](https://mlsanity.com/paper/2503.16718/) (2025)
- [Tackling Spoofing-Aware Speaker Verification with Multi-Model Fusion](https://mlsanity.com/paper/2206.09131/) (2022)
- [High-resolution embedding extractor for speaker diarisation](https://mlsanity.com/paper/2211.04060/) (2022)
- [DS-TDNN: Dual-stream Time-delay Neural Network with Global-aware Filter for Speaker Verification](https://mlsanity.com/paper/2303.11020/) (2023)
- [ACA-Net: Towards Lightweight Speaker Verification using Asymmetric Cross Attention](https://mlsanity.com/paper/2305.12121/) (2023)
- [Encoder-decoder multimodal speaker change detection](https://mlsanity.com/paper/2306.00680/) (2023)
- [Self-supervised Reflective Learning through Self-distillation and Online Clustering for Speaker Representation Learning](https://mlsanity.com/paper/2401.01473/) (2024)
- [To what extent can ASV systems naturally defend against spoofing attacks?](https://mlsanity.com/paper/2406.05339/) (2024)
- [MR-RawNet: Speaker verification system with multiple temporal resolutions for variable duration utterances using raw waveforms](https://mlsanity.com/paper/2406.07103/) (2024)

## Abstract (arXiv)

In this paper, we present Multi-scale Feature Aggregation Conformer (MFA-Conformer), an easy-to-implement, simple but effective backbone for automatic speaker verification based on the Convolution-augmented Transformer (Conformer). The architecture of the MFA-Conformer is inspired by recent stateof-the-art models in speech recognition and speaker verification. Firstly, we introduce a convolution subsampling layer to decrease the computational cost of the model. Secondly, we adopt Conformer blocks which combine Transformers and convolution neural networks (CNNs) to capture global and local features effectively. Finally, the output feature maps from all Conformer blocks are concatenated to aggregate multi-scale representations before final pooling. We evaluate the MFA-Conformer on the widely used benchmarks. The best system obtains 0.64%, 1.29% and 1.63% EER on VoxCeleb1-O, SITW.Dev, and SITW.Eval set, respectively. MFA-Conformer significantly outperforms the popular ECAPA-TDNN systems in both recognition performance and inference speed. Last but not the least, the ablation studies clearly demonstrate that the combination of global and local feature learning can lead to robust and accurate speaker embedding extraction. We have also released the code for future comparison.