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.

Takeaways

1. 1

   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.

2. 2

   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.

3. 3

   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.