A Computational Analysis of Lyric Similarity Perception
Haven Kim Taketo Akama
In musical compositions that include vocals, lyrics significantly contribute
to artistic expression. Consequently, previous studies have introduced the
concept of a recommendation system that suggests lyrics similar to a user's
favorites or personalized preferences, aiding in the discovery of lyrics among
millions of tracks. However, many of these systems do not fully consider human
perceptions of lyric similarity, primarily due to limited research in this
area. To bridge this gap, we conducted a comparative analysis of computational
methods for modeling lyric similarity with human perception. Results indicated
that computational models based on similarities between embeddings from
pre-trained BERT-based models, the audio from which the lyrics are derived, and
phonetic components are indicative of perceptual lyric similarity. This finding
underscores the importance of semantic, stylistic, and phonetic similarities in
human perception about lyric similarity. We anticipate that our findings will
enhance the development of similarity-based lyric recommendation systems by
offering pseudo-labels for neural network development and introducing objective
evaluation metrics.