TITLE: Dataset of raw and pre-processed speech signals, Mel Frequency Cepstral Coefficients of Speech and Heart Rate measurements 

ABSTRACT: Heart rate is an important vital sign used in the diagnosis of many medical
conditions. Conventionally, heart rate is measured using a medical device such
as pulse oxymeter. Physiological parameters such as heart rate bear a
correlation to speech characteristics of an individual. Hence, there is a
possibility to measure heart rate from speech signals using machine learning
and deep learning, which would also allow non-invasive, non contact based and
remote monitoring of patients. However, to design such a scheme and verify its
accuracy, it is necessary to collect speech recordings along with heart rates
measured using a medical device, simultaneously during the recording. This
article provides a dataset as well as the procedure used to create the dataset
which could be used to facilitate research in developing techniques to estimate
heart rate accurately by observing speech signal.
Heart rate is an important vital sign used in the diagnosis of many medical
conditions. Conventionally, heart rate is measured using a medical device such
as pulse oxymeter. Physiological parameters such as heart rate bear a
correlation to speech characteristics of an individual. Hence, there is a
possibility to measure heart rate from speech signals using machine learning
and deep learning, which would also allow non-invasive, non contact based and
remote monitoring of patients. However, to design such a scheme and verify its
accuracy, it is necessary to collect speech recordings along with heart rates
measured using a medical device, simultaneously during the recording. This
article provides a dataset as well as the procedure used to create the dataset
which could be used to facilitate research in developing techniques to estimate
heart rate accurately by observing speech signal.
Dataset of raw and pre-processed speech signals, Mel Frequency Cepstral Coefficients of Speech and Heart Rate measurements
http://arxiv.org/abs/2002.11250v1