Speech Signal Analysis for the Estimation of Heart Rates Under Different Emotional States

TL;DR

This paper introduces a non-invasive, speech-based method for estimating heart rates that can help monitor heart health and emotional states in real-time, using voice analysis and linear prediction models.

Contribution

It develops an empirical linear predictor model linking speech signals to heart rate, enabling dynamic, non-invasive heart rate estimation from voice data.

Findings

Prediction accuracy of about 80% on test data

Effective in estimating heart rate during different emotional states

Potential for real-time, non-invasive heart health monitoring

Abstract

A non-invasive method for the monitoring of heart activity can help to reduce the deaths caused by heart disorders such as stroke, arrhythmia and heart attack. The human voice can be considered as a biometric data that can be used for estimation of heart rate. In this paper, we propose a method for estimating the heart rate from human speech dynamically using voice signal analysis and by the development of an empirical linear predictor model. The correlation between the voice signal and heart rate are established by classifiers and prediction of the heart rates with or without emotions are done using linear models. The prediction accuracy was tested using the data collected from 15 subjects, it is about 4050 samples of speech signals and corresponding electrocardiogram samples. The proposed approach can use for early non-invasive detection of heart rate changes that can be correlated to an emotional state of the individual and also can be used as a tool for diagnosis of heart conditions in real-time situations.