# Low-rank Adaptation Method for Wav2vec2-based Fake Audio Detection

arXiv: https://arxiv.org/abs/2306.05617 · HTML: https://mlsanity.com/paper/2306.05617/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 12 references that are in the map.
- First posted on arXiv 9 Jun 2023.

## Builds on (most cited first, 9 of 9)

- [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://mlsanity.com/paper/2101.00190/) (2021)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [Learning multiple visual domains with residual adapters](https://mlsanity.com/paper/1705.08045/) (2017)
- [Unsupervised Cross-lingual Representation Learning for Speech Recognition](https://mlsanity.com/paper/2006.13979/) (2020)
- [A Survey on Deep Transfer Learning](https://mlsanity.com/paper/1808.01974/) (2018)
- [ASVspoof 2019: Future Horizons in Spoofed and Fake Audio Detection](https://mlsanity.com/paper/1904.05441/) (2019)
- [Investigating self-supervised front ends for speech spoofing countermeasures](https://mlsanity.com/paper/2111.07725/) (2021)
- [The Vicomtech Audio Deepfake Detection System based on Wav2Vec2 for the 2022 ADD Challenge](https://mlsanity.com/paper/2203.01573/) (2022)
- [Using Adapters to Overcome Catastrophic Forgetting in End-to-End Automatic Speech Recognition](https://mlsanity.com/paper/2203.16082/) (2022)

## Built on by (most cited first, 8 of 8)

- [Low-Rank Adaptation for Foundation Models: A Comprehensive Review](https://mlsanity.com/paper/2501.00365/) (2024)
- [Towards Higher Effective Rank in Parameter-efficient Fine-tuning using Khatri--Rao Product](https://mlsanity.com/paper/2508.00230/) (2025)
- [DAViD: Modeling Dynamic Affordance of 3D Objects Using Pre-trained Video Diffusion Models](https://mlsanity.com/paper/2501.08333/) (2025)
- [Generalizable speech deepfake detection via meta-learned LoRA](https://mlsanity.com/paper/2502.10838/) (2025)
- [Mixture of Low-Rank Adapter Experts in Generalizable Audio Deepfake Detection](https://mlsanity.com/paper/2509.13878/) (2025)
- [Audio Contrastive-based Fine-tuning: Decoupling Representation Learning and Classification](https://mlsanity.com/paper/2309.11895/) (2023)
- [An Unsupervised Domain Adaptation Method for Locating Manipulated Region in partially fake Audio](https://mlsanity.com/paper/2407.08239/) (2024)
- [Traceback Translators Against Forgetting in Continual Fake Speech Detection](https://mlsanity.com/paper/2607.12569/) (2026)

## Abstract (arXiv)

Self-supervised speech models are a rapidly developing research topic in fake audio detection. Many pre-trained models can serve as feature extractors, learning richer and higher-level speech features. However,when fine-tuning pre-trained models, there is often a challenge of excessively long training times and high memory consumption, and complete fine-tuning is also very expensive. To alleviate this problem, we apply low-rank adaptation(LoRA) to the wav2vec2 model, freezing the pre-trained model weights and injecting a trainable rank-decomposition matrix into each layer of the transformer architecture, greatly reducing the number of trainable parameters for downstream tasks. Compared with fine-tuning with Adam on the wav2vec2 model containing 317M training parameters, LoRA achieved similar performance by reducing the number of trainable parameters by 198 times.