  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. We review generative models built with neural networks
as well as architectures for creating neural networks that learn analogies. Our
preliminary results converting voices from one speaker to another are
encouraging.
