  Background music affects lyrics intelligibility of singing vocals in a music
piece. Automatic lyrics alignment and transcription in polyphonic music are
challenging tasks because the singing vocals are corrupted by the background
music. In this work, we propose to learn music genre-specific characteristics
to train polyphonic acoustic models. We first compare several automatic speech
recognition pipelines for the application of lyrics transcription. We then
present the lyrics alignment and transcription performance of music-informed
acoustic models for the best-performing pipeline, and systematically study the
impact of music genre and language model on the performance. With such
genre-based approach, we explicitly model the music without removing it during
acoustic modeling. The proposed approach outperforms all competing systems in
the lyrics alignment and transcription tasks on several well-known polyphonic
test datasets.
