  Lyrics transcription of polyphonic music is challenging because singing
vocals are corrupted by the background music. To improve the robustness of
lyrics transcription to the background music, we propose a strategy of
combining the features that emphasize the singing vocals, i.e. music-removed
features that represent singing vocal extracted features, and the features that
capture the singing vocals as well as the background music, i.e. music-present
features. We show that these two sets of features complement each other, and
their combination performs better than when they are used alone, thus improving
the robustness of the acoustic model to the background music. Furthermore,
language model interpolation between a general-purpose language model and an
in-domain lyrics-specific language model provides further improvement in
transcription results. Our experiments show that our proposed strategy
outperforms the existing lyrics transcription systems for polyphonic music.
Moreover, we find that our proposed music-robust features specially improve the
lyrics transcription performance in metal genre of songs, where the background
music is loud and dominant.
