# Exploring WavLM Back-ends for Speech Spoofing and Deepfake Detection

arXiv: https://arxiv.org/abs/2409.05032 · HTML: https://mlsanity.com/paper/2409.05032/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 25 references that are in the map.
- First posted on arXiv 8 Sep 2024.

## Builds on (most cited first, 20 of 20)

- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima](https://mlsanity.com/paper/1609.04836/) (2016)
- [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://mlsanity.com/paper/2111.09296/) (2021)
- [MUSAN: A Music, Speech, and Noise Corpus](https://mlsanity.com/paper/1510.08484/) (2015)
- [How to Fine-Tune BERT for Text Classification?](https://mlsanity.com/paper/1905.05583/) (2019)
- [YourTTS: Towards Zero-Shot Multi-Speaker TTS and Zero-Shot Voice Conversion for everyone](https://mlsanity.com/paper/2112.02418/) (2021)
- [AASIST: Audio Anti-Spoofing using Integrated Spectro-Temporal Graph Attention Networks](https://mlsanity.com/paper/2110.01200/) (2021)
- [End-to-end anti-spoofing with RawNet2](https://mlsanity.com/paper/2011.01108/) (2020)
- [In defence of metric learning for speaker recognition](https://mlsanity.com/paper/2003.11982/) (2020)
- [Automatic speaker verification spoofing and deepfake detection using wav2vec 2.0 and data augmentation](https://mlsanity.com/paper/2202.12233/) (2022)
- [RawBoost: A Raw Data Boosting and Augmentation Method applied to Automatic Speaker Verification Anti-Spoofing](https://mlsanity.com/paper/2111.04433/) (2021)
- [Investigating self-supervised front ends for speech spoofing countermeasures](https://mlsanity.com/paper/2111.07725/) (2021)
- [A Study On Data Augmentation In Voice Anti-Spoofing](https://mlsanity.com/paper/2110.10491/) (2021)
- [An attention-based backend allowing efficient fine-tuning of transformer models for speaker verification](https://mlsanity.com/paper/2210.01273/) (2022)
- [The BOSARIS Toolkit: Theory, Algorithms and Code for Surviving the New DCF](https://mlsanity.com/paper/1304.2865/) (2013)
- [Adapting WavLM for Speech Emotion Recognition](https://mlsanity.com/paper/2405.04485/) (2024)
- [Towards Supervised Performance on Speaker Verification with Self-Supervised Learning by Leveraging Large-Scale ASR Models](https://mlsanity.com/paper/2406.02285/) (2024)
- [Spoofing attack augmentation: can differently-trained attack models improve generalisation?](https://mlsanity.com/paper/2309.09586/) (2023)

## Built on by (most cited first, 10 of 10)

- [Assessing the Impact of Speaker Identity in Speech Spoofing Detection](https://mlsanity.com/paper/2602.20805/) (2026)
- [Evolutionary Multi-Objective Fusion of Deepfake Speech Detectors](https://mlsanity.com/paper/2604.01330/) (2026)
- [Enhancing Multi-Corpus Training in SSL-Based Anti-Spoofing Models: Domain-Invariant Feature Extraction](https://mlsanity.com/paper/2603.18657/) (2026)
- [Multilingual Dataset Integration Strategies for Robust Audio Deepfake Detection: A SAFE Challenge System](https://mlsanity.com/paper/2508.20983/) (2025)
- [A SUPERB-Style Benchmark of Self-Supervised Speech Models for Audio Deepfake Detection](https://mlsanity.com/paper/2603.01482/) (2026)
- [A Comparison of SSL-Based Feature Extractors and Back-End Classifiers for Spoofing Detection: A Multi-Corpus Training and Cross-Linguistic Analysis](https://mlsanity.com/paper/2606.08669/) (2026)
- [Speaker-Invariant Representation Learning for Spoofing Detection via Gradient Reversal and A Variational Information Bottleneck](https://mlsanity.com/paper/2606.08678/) (2026)
- [RAT: Reference-Augmented Training for ASV Anti-Spoofing](https://mlsanity.com/paper/2606.10908/) (2026)
- [What Do Deepfake Speech Detectors Actually Hear?](https://mlsanity.com/paper/2606.10912/) (2026)
- [Linguistic Bias Mitigation for Spoofing Detection via Gradient Reversal and A Variational Information Bottleneck](https://mlsanity.com/paper/2606.31411/) (2026)

## Abstract (arXiv)

This paper describes our submitted systems to the ASVspoof 5 Challenge Track 1: Speech Deepfake Detection - Open Condition, which consists of a stand-alone speech deepfake (bonafide vs spoof) detection task. Recently, large-scale self-supervised models become a standard in Automatic Speech Recognition (ASR) and other speech processing tasks. Thus, we leverage a pre-trained WavLM as a front-end model and pool its representations with different back-end techniques. The complete framework is fine-tuned using only the trained dataset of the challenge, similar to the close condition. Besides, we adopt data-augmentation by adding noise and reverberation using MUSAN noise and RIR datasets. We also experiment with codec augmentations to increase the performance of our method. Ultimately, we use the Bosaris toolkit for score calibration and system fusion to get better Cllr scores. Our fused system achieves 0.0937 minDCF, 3.42% EER, 0.1927 Cllr, and 0.1375 actDCF.