Voice Cloning with Consent

Summary

Realistic voice generation presents significant deepfake risks alongside legitimate communication benefits. To prevent unauthorized voice duplication, a voice consent gate integrates verification directly into the voice cloning workflow. The system relies on a language model to generate novel sentence pairs containing explicit consent and phonetically diverse phrases on random everyday topics. Users must read these generated lines aloud through a direct microphone stream rather than uploading pre-recorded files, and an automatic speech recognition system verifies the utterance against the generated text. Once confirmed, the consenting audio snippet is passed directly to the voice-cloning text-to-speech model as the reference sample for synthesis.

Context

Realistic voice cloning technology allows generating synthetic speech from a few seconds of recorded audio. While offering benefits for accessibility and language learning, it introduces deepfake risks, such as deceptive robocalls, requiring mechanisms to prevent malicious use without speaker authorization.

Approach / What changed

The proposed voice consent gate uses a language model to generate unique, context-specific sentence pairs—one stating explicit consent and model details, and another providing phonetic diversity on a random topic. The user speaks the generated text into a direct microphone input, an automatic speech recognition (ASR) system verifies the speech against the prompt, and the verified consent audio is supplied directly to the text-to-speech (TTS) voice cloning model.

Takeaways

• A voice consent gate architecture links three components: a generator for unique consent sentences, automatic speech recognition (ASR) for verification, and a voice-cloning text-to-speech (TTS) system.
• Generating dynamic sentence pairs via a language model ensures each session has distinct text, preventing the reuse of old consent recordings while ensuring sufficient phonetic diversity for voice cloning.
• Restricting input to direct microphone capture rather than uploaded files limits the risk of using manipulated audio, though provenance checks and speaker embeddings remain areas for future hardening.

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