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Multi-Stage Approach for Stress Detection Using Speech Lexical Analysis
Chyan P.
Proceedings 2023 IEEE 7th International Conference on Information Technology Information Systems and Electrical Engineering Icitisee 2023
Abstract
Stress is one of the psychological problems that people most often experience. Stress that is not managed well and occurs for a prolonged time can lead to depression, negatively impacting a person's physical and mental health. Detecting and identifying stress is challenging, but it is necessary to provide appropriate early treatment for someone who experiences it. Many researchers have explored various technology-based approaches to detecting stress based on these needs. One approach currently being actively researched is stress detection technology using voice signals as an indicator to detect stress in a person. However, of these various studies, studies have yet to focus on exploring the use of voice properties optimally in terms of its properties as a signal wave and its output as a linguistic product. In this paper, we propose a multi-stage stress detection model through speech using a combination of speech signal characteristics and lexical aspects of speech. We employ the Mel-Spectrogram for the sound feature extraction required for stress detection from speech signals. Then, using Word2Vec and TF-IDF as input representations, we developed an NLP model for stress detection via lexical aspects of speech. The evaluation results of our proposed multi-stage stress detection model were able to detect stress well by showing an average accuracy and F1-Score of 91.7 and 91.2, respectively.