Quantizer-Aware Hierarchical Neural Codec Modeling for Speech Deepfake Detection
Neural audio codecs discretize speech via residual vector quantization (RVQ), forming a coarse-to-fine hierarchy across quantizers. While codec models have been explored for representation learning, their discrete structure remains underutilized in speech deepfake detection. In particular, different quantization levels capture complementary acoustic cues, where early quantizers encode coarse structure and later quantizers refine residual details that reveal synthesis artifacts. Existing systems either rely on continuous encoder features or ignore this quantizer-level hierarchy. We propose a hierarchy-aware representation learning framework that models quantizer-level contributions through...
| Authors | Jinyang Wu, Zihan Pan, Qiquan Zhang et al. |
|---|---|
| Primary category | cs.SD |
| Published | 10 March 2026 |
| Last updated at source | 10 March 2026 |
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