A Computational Evaluation Framework for Singable Lyric Translation
This work addresses the challenge of quantitatively assessing lyric translations for singability, which is important for music localization and cultural exchange, though it appears incremental as it builds on existing translation and music analysis methods.
The paper tackles the problem of evaluating singable lyric translation by developing a computational framework with four metrics for musical, linguistic, and cultural dimensions, and validates it using a dataset of aligned English, Japanese, and Korean lyrics.
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.