  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.
