Detecting gender differences in perception of emotion in crowdsourced data

TL;DR

This paper introduces a new framework for analyzing gender differences in emotion perception from speech data collected in natural settings, demonstrating significant perceptual differences between men and women.

Contribution

It presents a reliable framework for studying emotion perception from crowdsourced speech data and shows statistically significant gender differences in perception.

Findings

Significant gender differences in speech-based emotion perception.

Framework applicable to loosely annotated emotion perception data.

Addresses challenges of statistical analysis in crowdsourced data.

Abstract

Do men and women perceive emotions differently? Popular convictions place women as more emotionally perceptive than men. Empirical findings, however, remain inconclusive. Most prior studies focus on visual modalities. In addition, almost all of the studies are limited to experiments within controlled environments. Generalizability and scalability of these studies has not been sufficiently established. In this paper, we study the differences in perception of emotion between genders from speech data in the wild, annotated through crowdsourcing. While we limit ourselves to a single modality (i.e. speech), our framework is applicable to studies of emotion perception from all such loosely annotated data in general. Our paper addresses multiple serious challenges related to making statistically viable conclusions from crowdsourced data. Overall, the contributions of this paper are two fold: a reliable novel framework for perceptual studies from crowdsourced data; and the demonstration of statistically significant differences in speech-based emotion perception between genders.