研究歌唱声音的女性化感知，构建自动预测模型
Perceived Femininity in Singing Voice: Analysis and Prediction
- 通过问卷调查收集128人对歌声女性化的主观判断
- 发现不同群体对歌声女性化的感知存在显著差异
- 微调x-vector模型实现歌声女性化自动预测
本文关注歌唱声音中常被忽视的女性化感知问题。尽管已有研究探讨了语音中的女性化感知，但歌唱声音领域的相关研究仍属空白。该研究有助于揭示音乐内容中的性别偏见。为此，我们设计基于刺激的问卷调查以测量歌唱声音女性化感知（PSVF），共收集128名参与者的反馈。分析揭示了不同人口统计群体在感知上的差异。此外，我们提出一种通过微调x-vector模型实现自动PSVF预测的方法，为音乐内容分析中探索声音相关的性别刻板印象提供新工具，超越传统的二元性别分类。本研究深化了对歌唱声音女性化感知复杂性的理解，并提出可复用的自动化分析工具。
原文摘要 · Abstract (English)
This paper focuses on the often-overlooked aspect of perceived voice femininity in singing voices. While existing research has examined perceived voice femininity in speech, the same concept has not yet been studied in singing voice. The analysis of gender bias in music content could benefit from such study. To address this gap, we design a stimuli-based survey to measure perceived singing voice femininity (PSVF), and collect responses from 128 participants. Our analysis reveals intriguing insights into how PSVF varies across different demographic groups. Furthermore, we propose an automatic PSVF prediction model by fine-tuning an x-vector model, offering a novel tool for exploring gender stereotypes related to voices in music content analysis beyond binary sex classification. This study contributes to a deeper understanding of the complexities surrounding perceived femininity in singing voices by analyzing survey and proposes an automatic tool for future research.
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