# Robust Spoofed Speech Detection via Temporal Pyramid Modeling

Source: [Robust Spoofed Speech Detection via Temporal Pyramid Modeling](<https://www.sota2.com/research/paper/6a30d91bd33dfb23e783d2ae>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-06-17T05:00:21.724Z

Authors: Mahtab Masoudi Nezhad, Nima Karimian

Published: 2026-06-15T15:16:10.000Z

## Abstract

Spoofed speech detection is increasingly challenged by realistic synthesis, voice conversion, and replay attacks, with cross-dataset generalization remaining a major limitation. This work we propose a Temporal Pyramid Adapter that utilize parallel temporal convolutions with varying receptive fields to capture multi-scale spoofing cues, ranging from local artifacts to global prosodic irregularities. We also integrated self-supervised XLS-R representations combined with front-end adapters, including Mel, Sinc, and a Temporal Pyramid design for multi-scale temporal modeling. The proposed model is evaluated cross multiple benchmark including ASVspoof 2017, ASVspoof 2021 (DF/LA), PartialSpoof, DiffSSD, and multilingual HQ-MPSD datasets. Experimental results demonstrate that Temporal Pyramid model obtained AUC of 99.24% and a EER of 3.87% on the PartialSpoof database, which is significantly outperforming the base model and several SOTA baseline such as LCNN-BLSTM (9.87% EER) and TRACE (8.08% EER). Additionally, multilingual evaluations confirm that while spoofing artifact are independent from language. While self-supervised representations improve robustness, performance degrades under domain and language shifts, highlighting the need for better adaptation and calibration strategies.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Spoof Detection on PartialSpoof (test)](<https://www.sota2.com/research/sota/spoof-detection-on-partialspoof-test.md>): Spoof detection; PartialSpoof (test); AUC: 86.5.

- [Spoof Detection on ASVspoof 2021 (test)](<https://www.sota2.com/research/sota/spoof-detection-on-asvspoof-2021-test.md>): Spoof detection; ASVspoof 2021 (test); AUC: 82.99.

- [Spoofed Speech Detection on PartialSpoof (Overall)](<https://www.sota2.com/research/sota/spoofed-speech-detection-on-partialspoof-overall.md>): Spoofed Speech Detection; PartialSpoof (Overall); EER: 3.87.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2606.16837>)

[PDF](<https://arxiv.org/pdf/2606.16837.pdf>)