  Inspired by recent work on neural network image generation which rely on
backpropagation towards the network inputs, we present a proof-of-concept
system for speech texture synthesis and voice conversion based on two
mechanisms: approximate inversion of the representation learned by a speech
recognition neural network, and on matching statistics of neuron activations
between different source and target utterances. Similar to image texture
synthesis and neural style transfer, the system works by optimizing a cost
function with respect to the input waveform samples. To this end we use a
differentiable mel-filterbank feature extraction pipeline and train a
convolutional CTC speech recognition network. Our system is able to extract
speaker characteristics from very limited amounts of target speaker data, as
little as a few seconds, and can be used to generate realistic speech babble or
reconstruct an utterance in a different voice.
