title: POLICY DRIVEN GENERATIVE ADVERSARIAL NETWORKS FOR ACCENTED SPEECH GENERATION
abstract: In this paper, we propose the generation of accented speech using generative adversarial
  networks. Through this work we make two main contributions a) The
  ability to condition latent representations while generating realistic speech samples
  b) The ability to efficiently generate long speech samples by using a novel
  latent variable transformation module that is trained using policy gradients. Previous
  methods are limited in being able to generate only relatively short samples
  or are not very efficient at generating long samples. The generated speech samples
  are validated through a number of various evaluation measures viz, a WGAN
  critic loss and through subjective scores on user evaluations against competitive
  speech synthesis baselines and detailed ablation analysis of the proposed model.
  The evaluations demonstrate that the model generates realistic long speech samples
  conditioned on accent efficiently.
authors: Prannay Khosla Preethi Jyothi Vinay P. Namboodiri Mukundhan Srinivasan
hIndex: Vinay P. Namboodiri 21