Multi-Level Information Aggregation Based Graph Attention Networks Towards Fake Speech Detection
Bibliographic record
Abstract
It is widely acknowledged that distinguishing genuine speech from spoofed speech encompasses various subbands and temporal segments within speech signals. However, prevailing spoofing detection methods tend to oversimplify the relationships between these cues by employing linear models. In this paper, we introduce a multi-level information aggregation Graph Attention Networks (MiaGATs) to generate highly discriminative features for fake speech detection (FSD). In MiaGATs, each subband and temporal segment of a speech signal is represented as distinct nodes. MiaGATs incorporates channel information aggregation within each node to effectively harness the unique spectral and temporal characteristics during the feature encoding stage. In particular, MiaGATs address the interactions between nodes through indirect node aggregation and integrates both indirect and direct node aggregation by max-pooling operation. Experimental results on ASVspoof2019 and ASVspoof2021 LA databases show significant relative improvement compared to the current state-of-the-art. In comparison to the leading integrated spectro-temporal graph attention networks, MiaGATs gains an impressive performance improvement in various conditions, underscoring MiaGATs's position as a new benchmark in spoofing detection performance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".