sidmulajkar / sentiment-predictor-for-stress-detection

Voice stress analysis (VSA) aims to differentiate between stressed and non-stressed outputs in response to stimuli (e.g., questions posed), with high stress seen as an indication of deception. In this work, we propose a deep learning-based psychological stress detection model using speech signals. With increasing demands for communication between humans and intelligent systems, automatic stress detection is becoming an interesting research topic. Stress can be reliably detected by measuring the level of specific hormones (e.g., cortisol), but this is not a convenient method for the detection of stress in human- machine interactions. The proposed algorithm first extracts Mel- filter bank coefficients using pre-processed speech data and then predicts the status of stress output using a binary decision criterion (i.e., stressed or unstressed) using CNN (Convolutional Neural Network) and dense fully connected layer networks.

Hi My name is Siddhant Mulajkar

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sentiment-predictor-for-stress-detection's Issues

Can you help me with the audio file that you have used?

About some files which are not uploded

Hello,

Firstly, this project you have made is fantastic, I just wanted to let you know that some of the model files you are using havent been uploaded here, can you please check that once.
E:\8th sem\Final_Year_Project_Files\New Folder\Data_noiseNshift.h5
this one which you are taking from 8th sem, that folder is not present.