Stressed Speech Recognition Using Smartphone and Embedded Device Integration.

Stress is a state of emotional tension generated by a variety of factors such as work, study, family, and others. Stress can worsen and have an impact on health if it is not managed soon. Several studies have presented methods for detecting people's emotions through their voices. The goal of this study is to determine whether someone is stressed and how much stress he is under by listening to his voice. It is expected to be able to assess the amount of stress through sound utilizing MFCC feature extraction and artificial neural network machine learning. This system is powered by a Raspberry Pi 4 connected through Bluetooth to a microphone and an application on an Android phone. The smartphone application was designed to integrate with the embedded system and to display the prediction result. The dataset used in this research was SUSAS (Speech Under Simulated and Acute Stress) consisting of 1860 utterances. During the development of the artificial neural network model using 70% as training dataset, the accuracy only achieved 76%. However, the accuracy of the overall integrated system by utilizing 30 data taken from dataset reached 90%. Meanwhile, the test's average computing time is 2 seconds.