KeYric: Unsupervised Keywords Extraction and Expansion from Music for Coherent Lyrics Generation

Abstract

We address the challenge of enhancing coherence in generated lyrics from symbolic music, particularly for creating singing-based language learning materials. Coherence, defined as the quality of being logical and consistent, forming a unified whole, is crucial for lyrics at multiple levels - word, sentence, and full-text. Additionally, it involves lyrics' musicality - matching of style and sentiment of the music. To tackle this, we introduce KeYric, a novel system that leverages keyword skeletons to strengthen both coherence and musicality in lyrics generation. KeYric employs an innovative approach with an unsupervised keyword skeleton extractor and a graph-based skeleton expander, designed to produce a style-appropriate keyword skeleton from input music. This framework integrates the skeleton with the input music via a three-layer coherence mechanism, significantly enhancing lyric coherence by 5% in objective evaluations. Subjective assessments confirm that KeYric-generated lyrics are perceived as 19% more coherent and suitable for language learning through singing compared to existing models. Our analyses indicate that integrating genre-relevant elements, such as pitch, into music encoding is crucial, as musical genres significantly affect lyric coherence.