  Cover songs are alternate versions of a song by a different artist. Long
being a vital part of the music industry, cover songs significantly influence
music culture and are commonly heard in public venues. The rise of online music
platforms has further increased their prevalence, often as background music or
video soundtracks. While current automatic identification methods serve
adequately for original songs, they are less effective with cover songs,
primarily because cover versions often significantly deviate from the original
compositions. In this paper, we propose a novel method for cover song detection
that utilizes the lyrics of a song. We introduce a new dataset for cover songs
and their corresponding originals. The dataset contains 5078 cover songs and
2828 original songs. In contrast to other cover song datasets, it contains the
annotated lyrics for the original song and the cover song. We evaluate our
method on this dataset and compare it with multiple baseline approaches. Our
results show that our method outperforms the baseline approaches.
