Why the study?
Stress is a frequent psychological issue affecting around 264 million individuals, motivating the development of a deep learning-based stress detector model using audio signals to differentiate stressed and non-stressed speech.
A deep learning model using CNNs and Mel-frequency cepstral coefficients was developed to detect stress from speech audio signals.
Audio CNN stress detection is preliminary; leaves open validation before cardiovascular use.
Stress is a common issue for every person. Around 264 million individuals suffer from stress, which is one of the most frequent psychological issues. We present a deep learning-based stress detector model using audio 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 connected layers that are all related.Speech is transformed into spectrograms and they are fed to the Convolutional Neural Network (CNN) model. Features are extracted using Mel-frequency cepstral coefficients from pre-processed data.
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Vamsinath et al. (2022) studied this question.
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