Stress Detection in Women Using Speech Analysis

DOI

10.2991/978-94-6463-252-1_80How to use a DOI?

Keywords

Stress; Voice analyzer; Artificial Intelligence; Speech acoustics

Abstract

“One of the common issues that everyone deals with is stress. One of the most prevalent emotional states in people is stress. Positive stress can motivate women to make important achievements. Yet, stress may also be damaging and destructive, negatively affecting many facets of one's life. Stress makes it more challenging to mature as it becomes extreme or chronic. Women are more prone to exhibit a wide range of emotions. Heart rates, blood pressure, and skin temperatures may now be monitored via smartwatches. Still, measuring stress-related biomarkers requires an individual to provide a sample of their blood or other bodily fluid. Voice Stress Analysis (VSA) is a field of study examining how the brain responds to human stress by listening to women's voices while under stress. There is a lot of equipment available for detecting stress by keeping an eye on skin temperature, blood pressure, and heart rates, but for the most part, determining stress-related biomarkers still requires some degree of invasiveness. Given that physiological measurements have several limits, speech analysis makes stress assessment more alluring, especially given that it is currently both inexpensive and non-intrusive. With an Android app, stress can now be measured via speech signals that use software and artificial intelligence to analyze the pitch, jitter, energy, rate, frequency, length, and number of pauses among other speech and speech acoustic characteristics. The on-call dialogues are the main data source sent to the program, revealing whether the woman is under stress or not. Thus, utilizing speech using speech to measure stress has potential, but important validation, privacy, and ethical concerns must be suggested system can assist women by automatically identifying their stress from ordinary speech without any additional aid.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - A. Sharada
AU  - R. Mamatha
AU  - K. Meghana
AU  - A. Monika
PY  - 2023
DA  - 2023/11/09
TI  - Stress Detection in Women Using Speech Analysis
BT  - Proceedings of the Second International Conference on Emerging Trends in Engineering (ICETE 2023)
PB  - Atlantis Press
SP  - 797
EP  - 808
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-252-1_80
DO  - 10.2991/978-94-6463-252-1_80
ID  - Sharada2023
ER  -