STRESS AND ANXIETY DETECTION THROUGH SPEECH RECOGNITION USING DEEP NEURAL NETWORK
Author(s):
Prajwal.B, Ghanavi Yadav A, Divyashree P, Namratha Jayadev, Sharmila Chidaravalli
Keywords:
Convolutional Neural Network, Emotion Classification, Stress Detection, MFCC (Mel frequency cepstral coefficients), Chroma
Abstract
Stress is a feeling of emotional tension. It can have an influence on our mental health and for the people around us. While anxiety is a natural reaction to stress which can be fearful this can lead to panic attacks. These mental issues have to be addressed by everyone. This paper explains how we are using vocal/audio dataset to detect stress and anxiety in a person. We have developed a stress and anxiety detection model using deep neural network. Here audio datasets is considered from Kaggle in which the audio consists of 7 emotions i.e., joy, fear, disgust, neutral, sadness, surprised and anger. These audio datasets are used to train and test classification models like CNN. Then the audio is pre-processed through acoustic feature extraction, classified through CNN which provides the accuracy based on those 7 emotions. By this we can predict if the person is stressed or has anxiety
Article Details
Unique Paper ID: 154638

Publication Volume & Issue: Volume 8, Issue 11

Page(s): 730 - 734
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