# Phase-Aware Spoof Speech Detection Based on Res2Net with Phase Network

arXiv: https://arxiv.org/abs/2203.10793 · HTML: https://mlsanity.com/paper/2203.10793/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 39 references that are in the map.
- First posted on arXiv 21 Mar 2022.

## Builds on (most cited first, 13 of 13)

- [AASIST: Audio Anti-Spoofing using Integrated Spectro-Temporal Graph Attention Networks](https://mlsanity.com/paper/2110.01200/) (2021)
- [ASVspoof 2019: Future Horizons in Spoofed and Fake Audio Detection](https://mlsanity.com/paper/1904.05441/) (2019)
- [End-to-end anti-spoofing with RawNet2](https://mlsanity.com/paper/2011.01108/) (2020)
- [UnivNet: A Neural Vocoder with Multi-Resolution Spectrogram Discriminators for High-Fidelity Waveform Generation](https://mlsanity.com/paper/2106.07889/) (2021)
- [End-to-End Spectro-Temporal Graph Attention Networks for Speaker Verification Anti-Spoofing and Speech Deepfake Detection](https://mlsanity.com/paper/2107.12710/) (2021)
- [A Comparative Study on Recent Neural Spoofing Countermeasures for Synthetic Speech Detection](https://mlsanity.com/paper/2103.11326/) (2021)
- [Replay and Synthetic Speech Detection with Res2net Architecture](https://mlsanity.com/paper/2010.15006/) (2020)
- [Deep Residual Neural Networks for Audio Spoofing Detection](https://mlsanity.com/paper/1907.00501/) (2019)
- [ASSERT: Anti-Spoofing with Squeeze-Excitation and Residual neTworks](https://mlsanity.com/paper/1904.01120/) (2019)
- [Sequence-to-Sequence Acoustic Modeling for Voice Conversion](https://mlsanity.com/paper/1810.06865/) (2018)
- [Channel-wise Gated Res2Net: Towards Robust Detection of Synthetic Speech Attacks](https://mlsanity.com/paper/2107.08803/) (2021)
- [Spoofing Attack Detection using the Non-linear Fusion of Sub-band Classifiers](https://mlsanity.com/paper/2005.10393/) (2020)
- [Replay attack detection with complementary high-resolution information using end-to-end DNN for the ASVspoof 2019 Challenge](https://mlsanity.com/paper/1904.10134/) (2019)

## Built on by (most cited first, 3 of 3)

- [Nes2Net: A Lightweight Nested Architecture for Foundation Model Driven Speech Anti-spoofing](https://mlsanity.com/paper/2504.05657/) (2025)
- [VoiceWukong: Benchmarking Deepfake Voice Detection](https://mlsanity.com/paper/2409.06348/) (2024)
- [Lightweight Model Attribution and Detection of Synthetic Speech via Audio Residual Fingerprints](https://mlsanity.com/paper/2411.14013/) (2024)

## Abstract (arXiv)

The spoof speech detection (SSD) is the essential countermeasure for automatic speaker verification systems. Although SSD with magnitude features in the frequency domain has shown promising results, the phase information also can be important to capture the artefacts of certain types of spoofing attacks. Thus, both magnitude and phase features must be considered to ensure the generalization ability to diverse types of spoofing attacks. In this paper, we investigate the failure reason of feature-level fusion of the previous works through the entropy analysis from which we found that the randomness difference between magnitude and phase features is large, which can interrupt the feature-level fusion via backend neural network; thus, we propose a phase network to reduce that difference. Our SSD system: phase network equipped Res2Net achieved significant performance improvement, specifically in the spoofing attack for which the phase information is considered to be important. Also, we demonstrate our SSD system in both known- and unknown-kind SSD scenarios for practical applications.