Why the study?
The study was conducted to automate the process of psychological stress detection using human speech without the intervention of a doctor or psychiatrist.
This project proposes an automated, deep learning-based approach to detect psychological stress from speech signals without clinical intervention.
May support exploratory speech-based stress screening; leaves open clinical validation before any practice consideration.
The project's goal is to develop a model for stress detection of humans using speech. We present a deep learning-based psychological stress detector model using speech signals. The main aim is to differentiate stressed and non-stressed speeches. The deep learning algorithm used here is Convolutional Neural Network(CNN) which is made up of layers that are all related. Speech is transformed into spectrograms and they are fed to the Convolutional Neural Network(CNN) model. Mel-frequency cepstral coefficients are used to extract features from pre-processed data. The results of this model can then be predicted exactly using binary decision criterion. The levels of particular hormones like cortisol are being used to consistently detect stress. In Fact the aim of this project is to automate the process of stress detection without the intervention of a Doctor or Psychiatrist. This project proposes a hybrid deep learning model to analyze whether the person is stressed or unstressed using speech.
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Vamsinath et al. (2022) studied this question.
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