A Computational Evaluation Framework for Singable Lyric Translation
Haven Kim Kento Watanabe Masataka Goto Juhan Nam
Lyric translation plays a pivotal role in amplifying the global resonance of
music, bridging cultural divides, and fostering universal connections.
Translating lyrics, unlike conventional translation tasks, requires a delicate
balance between singability and semantics. In this paper, we present a
computational framework for the quantitative evaluation of singable lyric
translation, which seamlessly integrates musical, linguistic, and cultural
dimensions of lyrics. Our comprehensive framework consists of four metrics that
measure syllable count distance, phoneme repetition similarity, musical
structure distance, and semantic similarity. To substantiate the efficacy of
our framework, we collected a singable lyrics dataset, which precisely aligns
English, Japanese, and Korean lyrics on a line-by-line and section-by-section
basis, and conducted a comparative analysis between singable and non-singable
lyrics. Our multidisciplinary approach provides insights into the key
components that underlie the art of lyric translation and establishes a solid
groundwork for the future of computational lyric translation assessment.