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The prevalence of voice spoofing attacks in today’s digital world has become a critical security concern. Attackers employ various techniques, such as voice conversion (VC) and text-to-speech (TTS), to generate synthetic speech that imitates the victim’s voice and gain access to sensitive information. The recent advances in synthetic speech generation pose a significant threat to modern security systems, while traditional voice authentication methods are incapable of detecting them effectively. To address this issue, a novel solution for logical access (LA)-based synthetic speech detection is proposed in this paper. SpoTNet is an attention-based spoofing transformer network that includes crafted front-end spoofing features and deep attentive features retrieved using the developed logical spoofing transformer encoder (LSTE). The derived attentive features were then processed by the proposed multi-layer spoofing classifier to classify speech samples as bona fide or synthetic. In synthetic speeches produced by the TTS algorithm, the spectral characteristics of the synthetic speech are altered to match the target speaker’s formant frequencies, while in VC attacks, the temporal alignment of the speech segments is manipulated to preserve the target speaker’s prosodic features. By highlighting these observations, this paper targets the prosodic and phonetic-based crafted features, i.e., the Mel-spectrogram, spectral contrast, and spectral envelope, presenting an effective preprocessing pipeline proven to be effective in synthetic speech detection. The proposed solution achieved state-of-the-art performance against eight recent feature fusion methods with lower EER of 0.95% on the ASVspoof-LA dataset, demonstrating its potential to advance the field of speaker identification and improve speaker recognition systems.
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This content will become publicly available on June 23, 2026
LiveGuard: Voice Liveness Detection via Wavelet Scattering Transform and Mel Spectrogram Scaling
Voice-controlled interfaces are essential in modern smart devices, but they remain vulnerable to replay attacks that compromise voice authentication systems. Existing voice liveness detection methods often struggle to distinguish human speech from replayed audio. This paper introduces a novel approach, LiveGuard, utilizing wavelet scattering transform (WST) and Mel spectrogram scaling with a lightweight ResNet architecture to enhance voice liveness detection. WST captures robust hierarchical features, while Mel spectrogram scaling extracts fine-grained acoustic details, which the lightweight ResNet efficiently processes to identify live voice. Experimental results demonstrate accuracy improvements of 6% with WST and Mel spectrogram scaling, achieving a top accuracy of 97.17% on POCO dataset. Meanwhile, LiveGuard demonstrates superior performance on ASVspoof2019 and ASVspoof2021 benchmarks. It achieves the lowest equal error rate (EER) of 0.13%, and a min t-DCF of 0.00126 on ASVspoof2019, and an EER of 0.42% on ASVspoof2021, surpassing state-of-the-art methods.
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- PAR ID:
- 10647749
- Publisher / Repository:
- IEEE
- Date Published:
- Page Range / eLocation ID:
- 317 to 330
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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