  Voice impersonation is not the same as voice transformation, although the
latter is an essential element of it. In voice impersonation, the resultant
voice must convincingly convey the impression of having been naturally produced
by the target speaker, mimicking not only the pitch and other perceivable
signal qualities, but also the style of the target speaker. In this paper, we
propose a novel neural network based speech quality- and style- mimicry
framework for the synthesis of impersonated voices. The framework is built upon
a fast and accurate generative adversarial network model. Given spectrographic
representations of source and target speakers' voices, the model learns to
mimic the target speaker's voice quality and style, regardless of the
linguistic content of either's voice, generating a synthetic spectrogram from
which the time domain signal is reconstructed using the Griffin-Lim method. In
effect, this model reframes the well-known problem of style-transfer for images
as the problem of style-transfer for speech signals, while intrinsically
addressing the problem of durational variability of speech sounds. Experiments
demonstrate that the model can generate extremely convincing samples of
impersonated speech. It is even able to impersonate voices across different
genders effectively. Results are qualitatively evaluated using standard
procedures for evaluating synthesized voices.
