Generative Adversarial Networks (GANs) have revolutionized high-fidelity image generation, making global headlines with their hyperrealistic portraits and content-swapping, while also raising concerns with convincing deepfake videos. Now, DeepMind researchers are expanding GANs to audio, with a new adversarial network approach for high fidelity speech synthesis. Text-to-Speech (TTS) is a process for converting text into a humanlike voice output. One of the most commonly used TTS network architectures is WaveNet, a neural autoregressive model for generating raw audio waveforms. But because WaveNet relies on the sequential generation of one audio sample at a time, it is poorly suited to today's massively parallel computers.

Generative Adversarial Networks (GANs) have revolutionized high-fidelity image generation, making global headlines with their hyperrealistic portraits and content-swapping, while also raising concerns with convincing deepfake videos. Now, DeepMind researchers are expanding GANs to audio, with a new adversarial network approach for high fidelity speech synthesis. Text-to-Speech (TTS) is a process for converting text into a humanlike voice output. One of the most commonly used TTS network architectures is WaveNet, a neural autoregressive model for generating raw audio waveforms. But because WaveNet relies on the sequential generation of one audio sample at a time, it is poorly suited to today's massively parallel computers.