  We propose a sequence-to-sequence singing synthesizer, which avoids the need
for training data with pre-aligned phonetic and acoustic features. Rather than
the more common approach of a content-based attention mechanism combined with
an autoregressive decoder, we use a different mechanism suitable for
feed-forward synthesis. Given that phonetic timings in singing are highly
constrained by the musical score, we derive an approximate initial alignment
with the help of a simple duration model. Then, using a decoder based on a
feed-forward variant of the Transformer model, a series of self-attention and
convolutional layers refines the result of the initial alignment to reach the
target acoustic features. Advantages of this approach include faster inference
and avoiding the exposure bias issues that affect autoregressive models trained
by teacher forcing. We evaluate the effectiveness of this model compared to an
autoregressive baseline, the importance of self-attention, and the importance
of the accuracy of the duration model.
