  Generative adversarial networks have seen rapid development in recent years
and have led to remarkable improvements in generative modelling of images.
However, their application in the audio domain has received limited attention,
and autoregressive models, such as WaveNet, remain the state of the art in
generative modelling of audio signals such as human speech. To address this
paucity, we introduce GAN-TTS, a Generative Adversarial Network for
Text-to-Speech. Our architecture is composed of a conditional feed-forward
generator producing raw speech audio, and an ensemble of discriminators which
operate on random windows of different sizes. The discriminators analyse the
audio both in terms of general realism, as well as how well the audio
corresponds to the utterance that should be pronounced. To measure the
performance of GAN-TTS, we employ both subjective human evaluation (MOS - Mean
Opinion Score), as well as novel quantitative metrics (Fr\'echet DeepSpeech
Distance and Kernel DeepSpeech Distance), which we find to be well correlated
with MOS. We show that GAN-TTS is capable of generating high-fidelity speech
with naturalness comparable to the state-of-the-art models, and unlike
autoregressive models, it is highly parallelisable thanks to an efficient
feed-forward generator. Listen to GAN-TTS reading this abstract at
https://storage.googleapis.com/deepmind-media/research/abstract.wav.
