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Dataset of Raw and Pre-processed Speech Signals, Mel Frequency Cepstral Coefficients of Speech and Heart Rate Measurements

机译:原始和预处理语音信号的数据集,语音的梅尔频率倒谱系数和心率测量

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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 oximeter. 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 noninvasive, 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.
机译:心率是诊断许多医疗状况的重要生命体征。常规地,使用诸如脉搏血氧仪的医疗设备来测量心率。诸如心率之类的生理参数与个人的言语特征相关。因此,有可能使用机器学习和深度学习从语音信号测量心率,这也将允许对患者进行非侵入性,非接触式和远程监控。然而,为了设计这样的方案并验证其准确性,有必要在记录期间同时收集语音记录以及使用医疗设备测量的心率。本文提供了一个数据集以及用于创建该数据集的过程,该过程可用于促进对通过观察语音信号准确估算心率的技术的研究。

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