Speech to Speech Synthesis for Voice Impersonation
New AI model converts speech to speech for voice cloning, outperforming previous GAN-based approaches with more convincing results.
Researchers Bjorn Johnson and Jared Levy developed the Speech to Speech Synthesis Network (STSSN), a model that performs speech-to-speech style transfer for voice impersonation. Their system fuses speech recognition and synthesis technologies to generate realistic audio samples. Benchmarked against a generative adversarial network (GAN) performing similar tasks, STSSN produces more convincing voice impersonations, demonstrating significant progress in direct speech-to-speech processing despite noted capacity limitations in the architecture.
Why It Matters
Advances realistic voice cloning for content creation but raises urgent concerns about audio deepfakes and security.