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

Authors: Rishith Sadashiv T N, Abhishek Bedge, Saisha Suresh Bore, Jagabandhu Mishra, Mrinmoy Bhattacharjee, S R Mahadeva Prasanna

Published: 2025-08-01 19:20:18+00:00

AI Summary

This paper proposes a novel fake speech detection model that fuses self-supervised learning (SSL) speech embeddings with modulation spectrogram features using multi-head attention. The resulting fused representation is then fed into an AASIST network for classification, achieving significant performance improvements over a baseline model in both in-domain and out-of-domain scenarios.

Abstract

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.