  This paper proposes a generative moment matching network (GMMN)-based
post-filter that provides inter-utterance pitch variation for deep neural
network (DNN)-based singing voice synthesis. The natural pitch variation of a
human singing voice leads to a richer musical experience and is used in
double-tracking, a recording method in which two performances of the same
phrase are recorded and mixed to create a richer, layered sound. However,
singing voices synthesized using conventional DNN-based methods never vary
because the synthesis process is deterministic and only one waveform is
synthesized from one musical score. To address this problem, we use a GMMN to
model the variation of the modulation spectrum of the pitch contour of natural
singing voices and add a randomized inter-utterance variation to the pitch
contour generated by conventional DNN-based singing voice synthesis.
Experimental evaluations suggest that 1) our approach can provide perceptible
inter-utterance pitch variation while preserving speech quality. We extend our
approach to double-tracking, and the evaluation demonstrates that 2) GMMN-based
neural double-tracking is perceptually closer to natural double-tracking than
conventional signal processing-based artificial double-tracking is.
