Perceived Femininity in Singing Voice: Analysis and Prediction

TL;DR

This paper investigates perceived femininity in singing voices through a survey and introduces an automatic prediction model, revealing demographic variations and offering a new tool for gender-related music content analysis.

Contribution

It is the first study to analyze perceived femininity in singing voices and proposes a fine-tuned x-vector model for automatic prediction of PSVF.

Findings

PSVF varies across demographic groups

The x-vector model effectively predicts perceived femininity

Provides a new tool for gender stereotype analysis in music

Abstract

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.