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Asthma Detection Research Based on Voice Signal Processing and Machine Learning

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Empirical Investigation for Predicting Depression from Different Machine Learning Based Voice Recognition Techniques.

R Punithavathi¹, M Sharmila¹, T Avudaiappan²

¹Department of Information Technology, M.Kumarasamy College of Engineering (Autonomous), Karur, TN, India.

Evidence-Based Complementary and Alternative Medicine : Ecam

|April 18, 2022

Summary

This study explores using voice recordings and social media data for early depression detection in youth. Machine learning algorithms analyze voice signals to identify mental health concerns, aiming to improve early intervention and support.

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Area of Science:

Psychiatry and Mental Health
Computer Science and Engineering
Data Science and Analytics

Background:

Youth depression diagnosis is a growing societal challenge with often undetected symptoms.
Social media and mobile phone usage offer new avenues for emotion prediction and mental health analysis.
Existing emotion prediction systems struggle with real-time, accurate analysis due to computational demands.

Purpose of the Study:

To investigate machine learning algorithms for recognizing voice signals for depression detection in adolescents.
To explore the potential of voice acoustic measures captured via smartphones for reliable depression assessment.
To enhance early intervention strategies for youth mental health through technological solutions.

Main Methods:

Reviewing various machine learning algorithms applied to voice signal recognition.
Analyzing the feasibility of using smartphone-based voice recordings for depression prediction.
Examining the impact of telephonic standards on voice data reliability for mental health assessment.

Main Results:

Voice recordings and social media activity show potential for detecting depression symptoms.
Objective voice acoustic measures are reliably detectable via smartphones.
Telephonic standards significantly influence the quality and reliability of speech data.

Conclusions:

Machine learning analysis of voice signals holds promise for future depression detection in youth.
Developing accurate, real-time emotion prediction systems is crucial for youth mental well-being.
This research paves the way for innovative approaches to address youth mental health issues.