A universal adversarial perturbation framework claiming to protect speech against zero-shot voice cloning by degrading cloned outputs while preserving input naturalness.
Parallel Tacotron 2: A Non-Autoregressive Neural TTS Model with Differentiable Duration Modeling
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abstract
This paper introduces Parallel Tacotron 2, a non-autoregressive neural text-to-speech model with a fully differentiable duration model which does not require supervised duration signals. The duration model is based on a novel attention mechanism and an iterative reconstruction loss based on Soft Dynamic Time Warping, this model can learn token-frame alignments as well as token durations automatically. Experimental results show that Parallel Tacotron 2 outperforms baselines in subjective naturalness in several diverse multi speaker evaluations. Its duration control capability is also demonstrated.
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CloneShield: A Framework for Universal Perturbation Against Zero-Shot Voice Cloning
A universal adversarial perturbation framework claiming to protect speech against zero-shot voice cloning by degrading cloned outputs while preserving input naturalness.