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Towards Predicting Menstrual Cycle Phases Exploiting Paralinguistic Features

MCML Authors

Link to Profile Björn Schuller

Björn Schuller

Prof. Dr.

Principal Investigator

Health Informatics

Abstract

As a growing number of people focus on understanding their bodies, the menstrual cycle and its impact on reproduction are gaining attention. Several studies have shown that the voice changes during the menstrual cycle. However, existing research primarily employs comparative analysis to detect these differences. This paper proposes using machine learning methods to analyse paralinguistic features extracted from women’s voices for predicting menstrual cycle phases. We leverage available data recorded during the menstrual and late follicular phases of 44 naturally cycling women. Using eight paralinguistic features, we achieve an accuracy of 60%, showcasing the feasibility of classifying those two phases using speech signals. We discuss implications and suggest future research avenues, such as the need to use personalised approaches.

inproceedings

EMBC 2024

46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Orlando, FL, USA, Jul 15-19, 2024.

Authors

A. Spiesberger • A. Mallol-Ragolta • A. Triantafyllopoulos • B. W. Schuller

Links

DOI

Research Area

 B3 | Multimodal Perception

BibTeXKey: SMT+24

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