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.

该研究提出使用额外的语音和音乐相关特征，通过对大量独唱人声进行训练的声学模型来适应少量领域数据中的复调音乐，并利用常规声学特征一起实现鲁棒性，该策略在词汇边界对准错误的显著降低中取得了成效，特别是在具有长时间音乐插曲的复调数据上。