# Fusion of Modulation Spectrogram and SSL with Multi-head Attention for Fake Speech Detection

arXiv: https://arxiv.org/abs/2508.01034 · HTML: https://mlsanity.com/paper/2508.01034/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Aug 2025.

## Built on by (most cited first, 1 of 1)

- [FlowFake: Liquid Networks for Audio Deepfake Detection](https://mlsanity.com/paper/2606.19579/) (2026)

## Abstract (arXiv)

Fake speech detection systems have become a necessity to combat against speech deepfakes. Current systems exhibit poor generalizability on out-of-domain speech samples due to lack to diverse training data. In this paper, we attempt to address domain generalization issue by proposing a novel speech representation using self-supervised (SSL) speech embeddings and the Modulation Spectrogram (MS) feature. A fusion strategy is used to combine both speech representations to introduce a new front-end for the classification task. The proposed SSL+MS fusion representation is passed to the AASIST back-end network. Experiments are conducted on monolingual and multilingual fake speech datasets to evaluate the efficacy of the proposed model architecture in cross-dataset and multilingual cases. The proposed model achieves a relative performance improvement of 37% and 20% on the ASVspoof 2019 and MLAAD datasets, respectively, in in-domain settings compared to the baseline. In the out-of-domain scenario, the model trained on ASVspoof 2019 shows a 36% relative improvement when evaluated on the MLAAD dataset. Across all evaluated languages, the proposed model consistently outperforms the baseline, indicating enhanced domain generalization.