Organizations: School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Israel · Department of Electrical Engineering, Afeka the Academic College of Engineering, Israel · Avignon University, LIA, France
Self-supervised learning (SSL) countermeasures (CMs) have shown strong performance in recent years. However, they often show degraded performance while facing unseen spoofing attacks and mismatched conditions. This study examines the Voxtral audio-language model (ALM) framework for spoofing detection, as a step toward combining CM capabilities within the ALM framework. We analyze how Voxtral captures spoofing cues through audio-text processing and propose an instruction-guided approach that uses label-sequence likelihoods to evaluate bonafide and spoofed speech. Experiments on the ASVspoof databases show that without task-specific adaptation, the LLM layers emphasize semantic representations, reducing the separability of spoof-discriminative acoustic cues compared to the Whisper-based audio encoder. Consequently, spoofing-related information becomes less separable after language-model processing. We also applied lightweight adaptation using weight-decomposed low-rank adaptation (DoRA) to the Voxtral model and propose the Spooftral model, achieving an equal error rate (EER) of 4.25% on the ASVspoof5 evaluation set.
Figures & tables
Fig. 1 : Overview of the Spooftral model based on Voxtral architecture, where audio and text inputs are jointly processed and spoofing decisions are obtained with likelihood scoring.
Database
Set
bonafide
Spoof
ASVspoof2019 LA
Train
2,580
22,800
Dev.
2,548
22,296
Eval.
7,355
63,882
ASVspoof2021 LA
Eval.
14,816
133,360
ASVspoof2021 DF
Eval.
14,869
519,059
ASVspoof5
Train
18,797
163,560
TABLE I : Number of bonafide and spoofed utterances in ASVspoof2019 LA, ASVspoof2021 LA, ASVspoof2021 DF, and ASVspoof5 databases.
Fig. 2 : PaCMAP projections of audio adapter (left) and LLM (right) embeddings across ASVspoof5 sets: (a) training set, (b) Dev. set, (c) Eval. set.
Database
LLM
Audio Adapter
Dev.
Eval.
Dev.
Eval.
ASVspoof2019 LA
6.60 (5.96,7.09)
7.17 (6.91,7.55)
2.79 (2.37,3.07)
5.48 (5.20,5.73)
ASVspoof2021 LA
9.26 (8.74,9.84)
17.34 (16.97,17.70)
2.79 (2.37,3.07)
13.57 (13.30,13.87)
ASVspoof2021 DF
7.02 (6.40,7.50)
19.20 (18.96,19.52)
2.79 (2.37,3.07)
9.64 (9.49,9.86)
ASVspoof5
20.73 (20.49,20.96)
19.09 (18.98,19.19)
11.58 (11.40,11.74)
9.75 (9.68,9.83)
TABLE II : Linear probing performance after the audio adapter and LLM layers on ASVspoof databases (EER %). CI is shown below each value.
Database
Spooftral
Spooftral-Enc
Dev.
Eval.
Dev.
Eval.
ASVspoof2019 LA
0.02 (0.00,0.11)
0.41 (0.35,0.47)
0.03 (0.00,0.11)
0.40 (0.34,0.50)
ASVspoof2021 LA
0.02 (0.00,0.11)
6.46 (6.25,6.69)
0.03 (0.00,0.12)
6.70 (6.43,6.89)
ASVspoof2021 DF
0.05 (0.00,0.09)
3.88 (3.78,3.97)
0.05 (0.00,0.11)
4.76 (4.61,4.91)
ASVspoof5
4.50 (4.41,4.61)
4.25 (4.20,4.31)
5.29 (5.18,5.41)
5.08 (5.03,5.14)
TABLE III : Performance comparison of Spooftral and Spooftral-Enc across the ASVspoof databases (EER %). CI is shown below each value.
Instruction Interface
Dev. EER
Eval. EER
Prompt: “Classify the audio as real or fake.” Labels: real / fake
5.67 (5.56,5.82)
4.47 (4.42,4.52)
Prompt: “Is this speech spoof or bonafide?” Labels: bonafide / spoof
4.77 (4.66,4.89)
4.46 (4.39,4.53)
TABLE IV : Instruction interface ablation for Spooftral on ASVspoof5 without margin regularization (EER %). CI below each value.
System
Dev.
Eval.
S10 Fusion † [ 19 ]
–
11.24
Fusion of WavLM-ResNet18-SA † ∗ [ 57 ]
0.64
7.01
SSL-IVSPT ∗ [ 58 ]
0.76
5.99
SLIM † ∗ [ 59 ]
–
5.50
Best open-condition submission † ∗ [ 60 ]
–
2.59
Spooftral (LLM Attention DoRA)
7.89 (7.75,8.03)
7.01 (6.94,7.08)
TABLE V : Comparison of Spooftral and Spooftral-Enc versus other SSL-based CMs on ASVspoof5 (EER %). CI below each value.
Fig. 3 : Per-attack spoof detection rate (%) on the ASVspoof5 development and evaluation sets using the EER threshold of each set.
Lane Department of Computer Science and Electrical Engineering West Virginia University Morgantown, WV · Bellini College of Artificial Intelligence, Cybersecurity and Computing University of South Florida Tampa, FL