Cedric Chan | Searcharxiv

arXiv2025

Toward Objective and Interpretable Prosody Evaluation in Text-to-Speech: A Linguistically Motivated Approach

Prosody is essential for speech technology, shaping comprehension, naturalness, and expressiveness. However, current text-to-speech (TTS) systems still struggle to accurately capture human-like prosodic variation, in part because existing evaluation methods for prosody remain limited. Traditional metrics like Mean Opinion Score (MOS) are resource-intensive, inconsistent, and offer little insight into why a system sounds unnatural. This study introduces a linguistically informed, semi-automatic framework for evaluating TTS prosody through a two-tier architecture that mirrors human prosodic organization. The method uses quantitative linguistic criteria to evaluate synthesized speech against human speech corpora across multiple acoustic dimensions. By integrating discrete and continuous prosodic measures, it provides objective and interpretable metrics of both event placement and cue realization, while accounting for the natural variability observed across speakers and prosodic cues. Results show strong correlations with perceptual MOS ratings while revealing model-specific weaknesses that traditional perceptual tests alone cannot capture. This approach provides a principled path toward diagnosing, benchmarking, and ultimately improving the prosodic naturalness of next-generation TTS systems.

eess.AS↗

arXiv2025

BadPatches: Routing-Aware Backdoor Attacks on Vision Mixture-of-Experts

Mixture-of-Experts (MoE) architectures have gained significant traction for reducing computational costs in deep neural networks by activating only a sparse subset of parameters during inference. While this efficiency makes MoE highly attractive for scaling vision tasks, its patch-based processing mechanism inherently disrupts traditional, routing-agnostic backdoor attacks by fragmenting or discarding adversarial triggers. To expose the vulnerabilities of this architecture, we introduce BadPatches, a novel routing-aware trigger application strategy specifically designed for patch-based MoE (pMoE) models and MoE-based vision transformers. Rather than applying a global pattern across the entire image, BadPatches encapsulates triggers within targeted image patches, ensuring they are consistently routed to and processed by the active experts. Our evaluations demonstrate that BadPatches achieves a high Attack Success Rate (ASR) at lower poisoning rates than routing-agnostic triggers, reaching over 83.2% ASR with a poisoning rate of only 0.01%, and scaling to a 96.8% ASR at 0.05%, while preserving the model's clean accuracy. Furthermore, the attack remains effective in gray-box scenarios where the adversary lacks complete knowledge of the model's patch routing configuration. Finally, we evaluate fine-pruning as a potential defense mechanism, revealing that pruning alone is insufficient to mitigate the attack; successful backdoor removal strictly requires the fine-tuning stage. These findings highlight the fragility of sparse vision architectures and underscore the need for routing-aware defenses.

cs.CR↗