  Singing voice synthesis has been paid rising attention with the rapid
development of speech synthesis area. In general, a studio-level singing corpus
is usually necessary to produce a natural singing voice from lyrics and
music-related transcription. However, such a corpus is difficult to collect
since it's hard for many of us to sing like a professional singer. In this
paper, we propose an approach -- Learn2Sing that only needs a singing teacher
to generate the target speakers' singing voice without their singing voice
data. In our approach, a teacher's singing corpus and speech from multiple
target speakers are trained in a frame-level auto-regressive acoustic model
where singing and speaking share the common speaker embedding and style tag
embedding. Meanwhile, since there is no music-related transcription for the
target speaker, we use log-scale fundamental frequency (LF0) as an auxiliary
feature as the inputs of the acoustic model for building a unified input
representation. In order to enable the target speaker to sing without singing
reference audio in the inference stage, a duration model and an LF0 prediction
model are also trained. Particularly, we employ domain adversarial training
(DAT) in the acoustic model, which aims to enhance the singing performance of
target speakers by disentangling style from acoustic features of singing and
speaking data. Our experiments indicate that the proposed approach is capable
of synthesizing singing voice for target speaker given only their speech
samples.

Sound;Audio and Speech Processing