Conditional Speaker Normalization of Vocal Tract Shape Features for Robust Synthetic Speech Detection
The rapid advancement of text-to-speech and voice conversion technologies has significantly improved the quality of synthetic speech, posing increasing challenges for developing reliable detection countermeasures. This study proposes a synthetic speech detection approach based on Log-Area Ratios (LARs) as acoustic features that provide a direct representation of vocal-tract shape. The underlying premise is that synthetic speech may exhibit inconsistencies in the speech production process that manifest as unnatural vocal-tract configurations, which can be captured through LAR features. To mitigate the speaker-dependent nature of LARs, we introduce a Conditional Speaker Normalization module that conditions the normalization process on speaker embeddings derived from either speaker verification systems or a speaker-height estimation model. These embeddings are processed through a set of fully connected layers to generate a bounded scaling vector that modulates the LAR features, improving their robustness and discriminative power in the evaluated setting. Experimental results show that the proposed approach improves performance on the FoR and Logical Access protocol of ASVspoof 2019 datasets, particularly with the speaker-height-related conditioning representation. Phoneme-level Integrated Gradients analysis indicates higher relative attribution to specific articulation categories, especially vowels. These patterns are consistent with a possible reduction in speaker-dependent articulatory variability, although the results do not establish that anatomical height information alone causes the improvement.