  Speech recognition is a well developed research field so that the current
state of the art systems are being used in many applications in the software
industry, yet as by today, there still does not exist such robust system for
the recognition of words and sentences from singing voice. This paper proposes
a complete pipeline for this task which may commonly be referred as automatic
lyrics transcription (ALT). We have trained convolutional time-delay neural
networks with self-attention on monophonic karaoke recordings using a sequence
classification objective for building the acoustic model. The dataset used in
this study, DAMP - Sing! 300x30x2 [1] is filtered to have songs with only
English lyrics. Different language models are tested including MaxEnt and
Recurrent Neural Networks based methods which are trained on the lyrics of pop
songs in English. An in-depth analysis of the self-attention mechanism is held
while tuning its context width and the number of attention heads. Using the
best settings, our system achieves notable improvement to the state-of-the-art
in ALT and provides a new baseline for the task.
