Multilingual automatic lyrics transcription (ALT) is a challenging task due
to the limited availability of labelled data and the challenges introduced by
singing, compared to multilingual automatic speech recognition. Although some
multilingual singing datasets have been released recently, English continues to
dominate these collections. Multilingual ALT remains underexplored due to the
scale of data and annotation quality. In this paper, we aim to create a
multilingual ALT system with available datasets. Inspired by architectures that
have been proven effective for English ALT, we adapt these techniques to the
multilingual scenario by expanding the target vocabulary set. We then evaluate
the performance of the multilingual model in comparison to its monolingual
counterparts. Additionally, we explore various conditioning methods to
incorporate language information into the model. We apply analysis by language
and combine it with the language classification performance. Our findings
reveal that the multilingual model performs consistently better than the
monolingual models trained on the language subsets. Furthermore, we demonstrate
that incorporating language information significantly enhances performance.

基于可用数据集，本文旨在创建一个能够进行多语言自动歌词转录的系统，并通过扩展目标词汇集合将已被证明在英语自动歌词转录中有效的技术应用于多语言场景。通过语言分析结合语言分类性能，我们的研究发现多语言模型的性能连续优于仅针对语言子集进行训练的单语言模型，并且将语言信息纳入模型中显著提高性能。