用语音模型自动分割韩国民谣动机，发现结构差异与社会功能相关
Motive-level Analysis of Form-functions Association in Korean Folk song
- 用歌词音频微调语音识别模型，实现动机边界自动标注
- 分析856首民谣，发现集体劳动类歌曲的动机数量与持续时间熵更高
- 为口头音乐传统提供可扩展的量化分析方法，适合文化研究者
由于结构不规则和需人工标注，民谣音频的计算分析颇具挑战。本文提出一种通过在歌词音频上微调语音转录模型并结合动机边界标注，实现韩国民谣自动动机分割的方法。对856首歌曲的应用中，提取了动机数量和持续时间熵作为结构特征。统计分析表明，这些特征随歌曲社会功能系统性变化：与集体劳动相关的歌曲表现出不同于娱乐或个人场景歌曲的结构模式。本工作为口头音乐传统的定量结构分析提供了可扩展的方法。
原文摘要 · Abstract (English)
Computational analysis of folk song audio is challenging due to structural irregularities and the need for manual annotation. We propose a method for automatic motive segmentation in Korean folk songs by fine-tuning a speech transcription model on audio lyric with motif boundary annotation. Applying this to 856 songs, we extracted motif count and duration entropy as structural features. Statistical analysis revealed that these features vary systematically according to the social function of the songs. Songs associated with collective labor, for instance, showed different structural patterns from those for entertainment or personal settings. This work offers a scalable approach for quantitative structural analysis of oral music traditions.
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