Speech Signal Analysis for the Estimation of Heart Rates Under Different Emotional States
This work addresses the need for early non-invasive detection of heart rate changes correlated with emotional states, potentially aiding in real-time diagnosis of heart conditions, though it appears incremental as it builds on existing voice-based biometric analysis.
The paper tackled the problem of non-invasive heart rate monitoring by proposing a method to estimate heart rate from human speech using voice signal analysis and an empirical linear predictor model, achieving prediction accuracy tested on 4050 samples from 15 subjects.
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.