  Automatic lyrics to polyphonic audio alignment is a challenging task not only
because the vocals are corrupted by background music, but also there is a lack
of annotated polyphonic corpus for effective acoustic modeling. In this work,
we propose (1) using additional speech and music-informed features and (2)
adapting the acoustic models trained on a large amount of solo singing vocals
towards polyphonic music using a small amount of in-domain data. Incorporating
additional information such as voicing and auditory features together with
conventional acoustic features aims to bring robustness against the increased
spectro-temporal variations in singing vocals. By adapting the acoustic model
using a small amount of polyphonic audio data, we reduce the domain mismatch
between training and testing data. We perform several alignment experiments and
present an in-depth alignment error analysis on acoustic features, and model
adaptation techniques. The results demonstrate that the proposed strategy
provides a significant error reduction of word boundary alignment over
comparable existing systems, especially on more challenging polyphonic data
with long-duration musical interludes.
