  Recent automatic lyrics transcription (ALT) approaches focus on building
stronger acoustic models or in-domain language models, while the pronunciation
aspect is seldom touched upon. This paper applies a novel computational
analysis on the pronunciation variances in sung utterances and further proposes
a new pronunciation model adapted for singing. The singing-adapted model is
tested on multiple public datasets via word recognition experiments. It
performs better than the standard speech dictionary in all settings reporting
the best results on ALT in a capella recordings using n-gram language models.
For reproducibility, we share the sentence-level annotations used in testing,
providing a new benchmark evaluation set for ALT.
