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ASSERT: Anti-Spoofing with Squeeze-Excitation and Residual neTworks

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arxiv 1904.01120 v1 pith:ZIHY6ZII submitted 2019-04-01 cs.CL cs.LGcs.SDeess.AS

Cheng-I Lai , Nanxin Chen , Jesús Villalba , Najim Dehak This is my paper · ORCID
classification cs.CLcs.LGcs.SDeess.AS
keywords assertanti-spoofingasvspoofmodelsnetworksresidualsqueeze-excitationnetwork
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We present JHU's system submission to the ASVspoof 2019 Challenge: Anti-Spoofing with Squeeze-Excitation and Residual neTworks (ASSERT). Anti-spoofing has gathered more and more attention since the inauguration of the ASVspoof Challenges, and ASVspoof 2019 dedicates to address attacks from all three major types: text-to-speech, voice conversion, and replay. Built upon previous research work on Deep Neural Network (DNN), ASSERT is a pipeline for DNN-based approach to anti-spoofing. ASSERT has four components: feature engineering, DNN models, network optimization and system combination, where the DNN models are variants of squeeze-excitation and residual networks. We conducted an ablation study of the effectiveness of each component on the ASVspoof 2019 corpus, and experimental results showed that ASSERT obtained more than 93% and 17% relative improvements over the baseline systems in the two sub-challenges in ASVspooof 2019, ranking ASSERT one of the top performing systems. Code and pretrained models will be made publicly available.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks

    eess.AS 2026-08 conditional novelty 7.0 of 10

    A 260-hour emotional deepfake benchmark spanning 21 attack systems shows state-of-the-art speech deepfake detectors degrade badly on emotionally expressive and LALM-based spoofing.

  2. Can Emotion Fool Anti-spoofing?

    eess.AS 2025-05 conditional novelty 6.0 of 10

    Emotional synthetic speech from zero-shot TTS fools the pre-trained RawNet2 anti-spoofing model, and a gated ensemble of emotion-specialized detectors reduces the error and the emotion gap.

  3. Comprehensive Layer-wise Analysis of SSL Models for Audio Deepfake Detection

    eess.AS 2025-02 conditional novelty 5.0 of 10

    Across six self-supervised speech models and ten deepfake datasets, the first 4-12 transformer layers match full-model fake audio detection performance, reducing parameters by at least half.

  4. Parallel Stacked Aggregated Network for Voice Authentication in IoT-Enabled Smart Devices

    cs.SD 2024-11 conditional novelty 4.0 of 10

    PSA-Net, a light raw-audio network with ResNeXt-style aggregation and squeeze-and-excitation blocks, reports consistent error rates across voice cloning, replay, and chained replay attacks on four benchmarks.

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