Voice data is crucial for modern artificial intelligence (AI) systems, powering various applications from speaker recognition (SR) to AI-generated content. The reliance on massive data raises serious concerns about privacy and property rights due to potential unauthorized misuse. Unfortunately, no effective method has been proposed to protect the privacy and copyright of voice data while meeting perceptual audio quality requirements. To bridge this gap, we introduce Volto, a unified dual-function framework for generating unlearnable yet traceable voice examples. Volto jointly integrates unlearnable perturbations and learnable watermarks, exploiting weaknesses in both human auditory perception and deep neural network (DNN) representations. This synergistic design enables Volto to corrupt critical model-sensitive features, hindering unauthorized model learning, while encoding watermark signals that remain recoverable for ownership verification. The unlearnable examples can confuse DNNs while preserving the perceptual quality, reducing the accuracy of unauthorized models by more than 21.44% in black-box scenarios. Volto also introduces a verifiable watermark, providing robust evidence of data ownership. Our work effectively balances protection effectiveness and usability, offering an effective defense for voice privacy.