Multi-Sinkhorn Teacher Knowledge Aggregation Framework for Adaptive Audio Anti-Spoofing

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F26%3A0199981" target="_blank" >RIV/00216305:26230/26:0199981 - isvavai.cz</a>

Result on the web

<a href="https://ieeexplore.ieee.org/abstract/document/11150711" target="_blank" >https://ieeexplore.ieee.org/abstract/document/11150711</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TASLPRO.2025.3606191" target="_blank" >10.1109/TASLPRO.2025.3606191</a>

Result language

angličtina

Original language name

Multi-Sinkhorn Teacher Knowledge Aggregation Framework for Adaptive Audio Anti-Spoofing

Original language description

Audio anti-spoofing algorithms are widely deployed to defend against spoofing attacks, yet they often fail to detect unseen attacks. Although unsupervised domain adaptation (UDA) offers the potential to address this challenge, existing methods struggle with the large intra-class variability and complex distribution structures in target domains caused by the diversity of speech and attack types. In contrast, optimal transport (OT) leverages the geometric structure of intra-class distributions to measure discrepancies between probability distributions. The effectiveness of OT relies on the discriminability of data within target domains. However, in real-world scenarios involving multiple target domains, these domains often overlap in feature space, leading to the negative transport problem in OT. To overcome these domain mismatches in anti-spoofing, we propose the Multi-Sinkhorn Teacher Knowledge Aggregation (MSTKA) framework. Initially, to avoid interference between target domains during alignment, we use OT to adapt the source model to each target domain independently, thereby reducing negative transport. This adaptation involves constructing an OT cost matrix based on sentence-level representations of cross-domain samples and training an expert model for each target domain. Subsequently, we aggregate the knowledge from these expert models into a unified student model, enabling it to generalize across multiple target domains. Since spoofing cues could be distributed across different temporal scales, we align the student model's representations at multiple time scales with the teacher model's sentence-level representations to enhance the effectiveness of knowledge distillation. Multi-target adaptation experiments on eleven data sets demonstrate that our framework achieves state-of-the-art performance in audio anti-spoofing.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

IEEE Transactions on Audio, Speech, and Language Processing

ISSN

1558-7916

e-ISSN

1558-7924

Volume of the periodical

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Issue of the periodical within the volume

33

Country of publishing house

US - UNITED STATES

Number of pages

16

Pages from-to

3850-3865

UT code for WoS article

001579024300004

EID of the result in the Scopus database

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