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Pitch and timbre manipulations using cortical representation of sound
Dmitry N Zotkin, Shihab A Shamma, Powen Ru, Ramani Duraiswami, and Larry S Davis · 2003
Earlier work this paper cites.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Earlier work this paper cites.

Similar

Deep generative image models using a laplacian pyramid of adversarial networks
Emily L Denton, Soumith Chintala, Rob Fergus, et al · 2015
Cited alongside, same era.

Then

Deep visual analogy-making
Scott E Reed, Yi Zhang, Yuting Zhang, and Honglak Lee · 2015
Later among the works it cites.

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2016

Voice Conversion using Convolutional Neural Networks

Mobin, Shariq, Bruna, Joan

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The human auditory system is able to distinguish the vocal source of thousands of speakers, yet not much is known about what features the auditory system uses to do this.

Fourier Transforms are capable of capturing the pitch and harmonic structure of the speaker but this alone proves insufficient at identifying speakers uniquely.
The remaining structure, often referred to as timbre, is critical to identifying speakers but we understood little about it.
In this paper we use recent advances in neural networks in order to manipulate the voice of one speaker into another by transforming not only the pitch of the speaker, but the timbre.