  We propose the HumanGAN, a generative adversarial network (GAN) incorporating
human perception as a discriminator. A basic GAN trains a generator to
represent a real-data distribution by fooling the discriminator that
distinguishes real and generated data. Therefore, the basic GAN cannot
represent the outside of a real-data distribution. In the case of speech
perception, humans can recognize not only human voices but also processed
(i.e., a non-existent human) voices as human voice. Such a human-acceptable
distribution is typically wider than a real-data one and cannot be modeled by
the basic GAN. To model the human-acceptable distribution, we formulate a
backpropagation-based generator training algorithm by regarding human
perception as a black-boxed discriminator. The training efficiently iterates
generator training by using a computer and discrimination by crowdsourcing. We
evaluate our HumanGAN in speech naturalness modeling and demonstrate that it
can represent a human-acceptable distribution that is wider than a real-data
distribution.
