  End-to-end singing voice synthesis (SVS) is attractive due to the avoidance
of pre-aligned data. However, the auto learned alignment of singing voice with
lyrics is difficult to match the duration information in musical score, which
will lead to the model instability or even failure to synthesize voice. To
learn accurate alignment information automatically, this paper proposes an
end-to-end SVS framework, named Singing-Tacotron. The main difference between
the proposed framework and Tacotron is that the speech can be controlled
significantly by the musical score's duration information. Firstly, we propose
a global duration control attention mechanism for the SVS model. The attention
mechanism can control each phoneme's duration. Secondly, a duration encoder is
proposed to learn a set of global transition tokens from the musical score.
These transition tokens can help the attention mechanism decide whether moving
to the next phoneme or staying at each decoding step. Thirdly, to further
improve the model's stability, a dynamic filter is designed to help the model
overcome noise interference and pay more attention to local context
information. Subjective and objective evaluation verify the effectiveness of
the method. Furthermore, the role of global transition tokens and the effect of
duration control are explored. Examples of experiments can be found at
https://hairuo55.github.io/SingingTacotron.
