Year: 2026
Type: conference-paper
Abstract: Achieving robust generalization in speech deepfake detection (SDD) remains a primary challenge, as models often fail to detect unseen forgery methods.While research has focused on model-centric and algorithm-centric solutions, the impact of data composition is often underexplored.This paper proposes a datacentric approach, analyzing the SDD data landscape from two practical perspectives: constructing a single dataset and aggregating multiple datasets.To address the first perspective, we conduct a large-scale empirical study to characterize the data scaling laws for SDD, quantifying the impact of source and generator diversity.To address the second, we propose the Diversity-Optimized Sampling Strategy (DOSS), a principled framework for mixing heterogeneous data with two implementations: DOSS-Select (pruning) and DOSS-Weight (re-weighting).Our experiments show that DOSS-Select outperforms the naive aggregation baseline while using only 3% of the total available data.Furthermore, our final model, trained on a 12k-hour curated data pool using the optimal DOSS-Weight strategy, achieves state-of-the-art performance, outperforming large-scale baselines with greater data and model efficiency on both public benchmarks and a new challenge set of various commercial APIs.
Authors: Wen Huang, Yuchen Mao, Yanmin Qian
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Award: G5987393275
OpenAlex ID: W7166830663