# HumanGAN: generative adversarial network with human-based discriminator and its evaluation in speech perception modeling

arXiv: https://arxiv.org/abs/1909.11391 · HTML: https://mlsanity.com/paper/1909.11391/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Sep 2019.

## Built on by (most cited first, 1 of 1)

- [HumanACGAN: conditional generative adversarial network with human-based auxiliary classifier and its evaluation in phoneme perception](https://mlsanity.com/paper/2102.04051/) (2021)

## Abstract (arXiv)

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.