  We describe Parrotron, an end-to-end-trained speech-to-speech conversion
model that maps an input spectrogram directly to another spectrogram, without
utilizing any intermediate discrete representation. The network is composed of
an encoder, spectrogram and phoneme decoders, followed by a vocoder to
synthesize a time-domain waveform. We demonstrate that this model can be
trained to normalize speech from any speaker regardless of accent, prosody, and
background noise, into the voice of a single canonical target speaker with a
fixed accent and consistent articulation and prosody. We further show that this
normalization model can be adapted to normalize highly atypical speech from a
deaf speaker, resulting in significant improvements in intelligibility and
naturalness, measured via a speech recognizer and listening tests. Finally,
demonstrating the utility of this model on other speech tasks, we show that the
same model architecture can be trained to perform a speech separation task
