  This paper describes a method based on a sequence-to-sequence learning
(Seq2Seq) with attention and context preservation mechanism for voice
conversion (VC) tasks. Seq2Seq has been outstanding at numerous tasks involving
sequence modeling such as speech synthesis and recognition, machine
translation, and image captioning. In contrast to current VC techniques, our
method 1) stabilizes and accelerates the training procedure by considering
guided attention and proposed context preservation losses, 2) allows not only
spectral envelopes but also fundamental frequency contours and durations of
speech to be converted, 3) requires no context information such as phoneme
labels, and 4) requires no time-aligned source and target speech data in
advance. In our experiment, the proposed VC framework can be trained in only
one day, using only one GPU of an NVIDIA Tesla K80, while the quality of the
synthesized speech is higher than that of speech converted by Gaussian mixture
model-based VC and is comparable to that of speech generated by recurrent
neural network-based text-to-speech synthesis, which can be regarded as an
upper limit on VC performance.
