arXiv subjects

Yehuda Ben-Shimol

Publications and source records attributed to Yehuda Ben-Shimol.

6 recordsLinked to original sources

arXiv2026

ELEAT-SAGA: Early & Late Integration with Evading Alternating Training for Spoof-Robust Speaker Verification

Spoofing-robust automatic speaker verification (SASV) seeks to build automatic speaker verification systems that are robust against both zero-effort impostor attacks and sophisticated spoofing techniques such as voice conversion (VC) and text-to-speech (TTS). In this work, we propose a novel SASV architecture that introduces score-aware gated attention (SAGA), SASV-SAGA, enabling dynamic modulation of speaker embeddings based on countermeasure (CM) scores. By integrating speaker embeddings and CM scores from pre-trained ECAPA-TDNN and AASIST models respectively, we explore several integration strategies including early, late, and full integration. We further introduce alternating training for multi-module (ATMM) and a refined variant, evading alternating training (EAT). Experimental results on the ASVspoof 2019 Logical Access (LA) and Spoofceleb datasets demonstrate significant improvements over baselines, achieving a spoofing aware speaker verification equal error rate (SASV-EER) of 1.22% and minimum normalized agnostic detection cost function (min a-DCF) of 0.0304 on the ASVspoof 2019 evaluation set. These results confirm the effectiveness of score-aware attention mechanisms and alternating training strategies in enhancing the robustness of SASV systems.

eess.AS↗

arXiv2025

ASVspoof2019 vs. ASVspoof5: Assessment and Comparison

ASVspoof challenges are designed to advance the understanding of spoofing speech attacks and encourage the development of robust countermeasure systems. These challenges provide a standardized database for assessing and comparing spoofing-robust automatic speaker verification solutions. The ASVspoof5 challenge introduces a shift in database conditions compared to ASVspoof2019. While ASVspoof2019 has mismatched conditions only in spoofing attacks in the evaluation set, ASVspoof5 incorporates mismatches in both bona fide and spoofed speech statistics. This paper examines the impact of these mismatches, presenting qualitative and quantitative comparisons within and between the two databases. We show the increased difficulty for genuine and spoofed speech and demonstrate that in ASVspoof5, not only are the attacks more challenging, but the genuine speech also shifts toward spoofed speech compared to ASVspoof2019.

eess.AS↗

arXiv2025

ATMM-SAGA: Alternating Training for Multi-Module with Score-Aware Gated Attention SASV system

The objective of automatic speaker verification (ASV) systems is to determine whether a given test speech utterance corresponds to a claimed enrolled speaker. These systems have a wide range of applications, and ensuring their reliability is crucial. In this paper, we propose a spoofing-robust automatic speaker verification (SASV) system employing a score-aware gated attention (SAGA) fusion scheme, integrating scores from a pre-trained countermeasure (CM) with speaker embeddings from a pre-trained ASV. Specifically, we employ the AASIST and ECAPA-TDNN models. SAGA acts as an adaptive gating mechanism, where the CM score determines how strongly ASV embeddings influence the final SASV decision. Experiments on the ASVspoof2019 logical access dataset demonstrate that the proposed SASV system achieves an SASV equal error rate (SASV-EER) and agnostic detection cost function (a-DCF) of 2.31%, 0.0603 for the development set and 2.18%, 0.0480 for the evaluation set.

eess.AS↗

arXiv2024

Tandem spoofing-robust automatic speaker verification based on time-domain embeddings

Spoofing-robust automatic speaker verification (SASV) systems are a crucial technology for the protection against spoofed speech. In this study, we focus on logical access attacks and introduce a novel approach to SASV tasks. A novel representation of genuine and spoofed speech is employed, based on the probability mass function (PMF) of waveform amplitudes in the time domain. This methodology generates novel time embeddings derived from the PMF of selected groups within the training set. This paper highlights the role of gender segregation and its positive impact on performance. We propose a countermeasure (CM) system that employs time-domain embeddings derived from the PMF of spoofed and genuine speech, as well as gender recognition based on male and female time-based embeddings. The method exhibits notable gender recognition capabilities, with mismatch rates of 0.94% and 1.79% for males and females, respectively. The male and female CM systems achieve an equal error rate (EER) of 8.67% and 10.12%, respectively. By integrating this approach with traditional speaker verification systems, we demonstrate improved generalization ability and tandem detection cost function evaluation using the ASVspoof2019 challenge database. Furthermore, we investigate the impact of fusing the time embedding approach with traditional CM and illustrate how this fusion enhances generalization in SASV architectures.

eess.AS↗

arXiv2023

A Novel Reconfigurable Vector-Processed Interleaving Algorithm for a DVB-RCS2 Turbo Encoder

Turbo-Codes (TC) are a family of convolutional codes enabling Forward-Error-Correction (FEC) while approaching the theoretical limit of channel capacity predicted by Shannons theorem. One of the bottlenecks of a Turbo Encoder (TE) lies in the non-uniform interleaving stage. Interleaving algorithms require stalling the input vector bits before the bit rearrangement causing a delay in the overall process. This paper presents performance enhancement via a parallel algorithm for the interleaving stage of a Turbo Encoder application compliant with the DVB-RCS2 standard. The algorithm efficiently implements the interleaving operation while utilizing attributes of a given DSP. We will discuss and compare a serial model for the TE, with the presented parallel processed algorithm. Results showed a speed-up factor of up to 3.4 Total-Cycles, 4.8 Write and 7.3 Read.

cs.IT↗

arXiv2010

A system outlook on the vision problem associated with observation of light flickering at the micro-saccades' frequency

The flickering of the light of fluorescent lamps (FL), whose basic frequency, 100 Hz, is close to that of the micro-saccadic (the eye-muscles' tremor component) eye movement, is a severe problem for autists (autistic humans), a problem for newborn babies, for people after some traumatic accidents, and for some 10% of otherwise absolutely normal humans. Taking the line of a "system-terms" discussion of the vision-disturbance problem, the present work provides a constructive framework for investigating the problem. Using the results of light intensity measurements and some simple analytical models for the instantaneous light-intensity function, and analyzing the role of the coincidence (closeness) of the frequencies of the ripple of and the micro saccades, we suggest a block-diagram for the biological vision control system. We also show that a singularity of the waveform of, which is typical for most FL, may be important for brain control of the eye tremor. The latter conclusion is supported by a simulation of the image intensity on retina.

physics.med-ph↗