  This paper presents a method of using autoregressive neural networks for the
acoustic modeling of singing voice synthesis (SVS). Singing voice differs from
speech and it contains more local dynamic movements of acoustic features, e.g.,
vibratos. Therefore, our method adopts deep autoregressive (DAR) models to
predict the F0 and spectral features of singing voice in order to better
describe the dependencies among the acoustic features of consecutive frames.
For F0 modeling, discretized F0 values are used and the influences of the
history length in DAR are analyzed by experiments. An F0 post-processing
strategy is also designed to alleviate the inconsistency between the predicted
F0 contours and the F0 values determined by music notes. Furthermore, we extend
the DAR model to deal with continuous spectral features, and a prenet module
with self-attention layers is introduced to process historical frames.
Experiments on a Chinese singing voice corpus demonstrate that our method using
DARs can produce F0 contours with vibratos effectively, and can achieve better
objective and subjective performance than the conventional method using
recurrent neural networks (RNNs).
