Why stress detection through speech is better? 

Stress detection through speech offers several advantages over traditional methods, making it a superior approach in many aspects. Firstly, it is non-invasive and contactless, which addresses the issues associated with other stress detection methods that might require physical contact or invasive procedures, thus ensuring the comfort and ease of individuals undergoing stress detection . The use of speech as a medium for detecting stress leverages the natural variation in speech patterns when an individual is stressed, making it a reliable indicator of stress without requiring any physical intervention . Moreover, the application of advanced machine learning algorithms, such as Convolutional Neural Networks (CNN), to speech signals has demonstrated high accuracy in classifying stressed and non-stressed states. This is evidenced by the high classification accuracies achieved in studies, which have reported accuracies up to 95.83% and 95.37% for male and female speakers, respectively, using Teager-MFCC (T-MFCC) features . Such high levels of accuracy underscore the effectiveness of speech-based stress detection systems. Additionally, the flexibility of speech-based systems to incorporate various auditory features, including traditional features like Mel-Frequency Cepstral Coefficients (MFCC) and modern non-semantic speech representations, further enhances their capability to accurately detect stress . This adaptability allows for the refinement and improvement of stress detection models as new research and technologies emerge. The use of deep learning models, particularly those based on CNN architecture, has also been shown to accurately detect stress through voice recording, achieving accuracy values as high as 97.1% . This indicates the potential of speech-based stress detection systems to operate with high precision across different datasets and conditions. Furthermore, the ability to detect stress through speech enables early detection and prevention of stress-related ailments, offering a proactive approach to managing stress and its associated health risks . By integrating speech-based stress detection into affective computing and human-machine interaction, it is possible to enhance the well-being and mental health support provided to individuals . In conclusion, stress detection through speech is advantageous due to its non-invasiveness, high accuracy, adaptability to incorporate various auditory features, and potential for early stress detection and intervention, making it a promising approach for managing stress and its impacts on health .

Investigation and ordinal modelling of vocal features for stress detection in speech

Stress detection through speech is advantageous due to the effectiveness of vocal features in identifying stress, as shown by the study achieving up to 95.0% accuracy using ordinal modeling.

A Deep Learning Approach for Stress Detection Through Speech with Audio Feature Analysis

Stress detection through speech is effective due to a deep learning model achieving 97.1% accuracy using sound features like Mel Spectrogram and MFCC, proving accurate stress identification through voice.

Stress Identification from Speech Using Clustering Techniques

Stress detection through speech is better due to the ability to identify emotional states, where clustering techniques like Vector Quantization outperform MFCC, as shown in the research.

A Deep Learning Approach for Stress Detection Through Speech with Audio Feature Analysis

Stress detection through speech is effective due to a deep learning model achieving 97.1% accuracy using sound features like Mel Spectrogram and MFCC, proving accurate stress identification through voice.

Acoustic based Stress level identification using Deep Neural architecture

Stress detection through speech is advantageous due to its simplicity, cost-effectiveness, and the utilization of features like MFCC, TEO, TEO-CB, and TEO-PWP for accurate stress identification.

Stress Detection Through Speech Analysis Using Machine Learning

Stress detection through speech is advantageous due to its ability to differentiate stressed and non-stressed speeches using a deep learning-based model with Mel-frequency cepstral coefficients for feature extraction.

Stress detection using non-semantic speech representation

Stress detection through speech is beneficial for early identification and prevention of stress-related issues, aiding in automatic stress recognition using advanced auditory features and machine learning algorithms.

Stress detection using non-semantic speech representation

Stress detection through speech is beneficial for early prevention of stress-related ailments. Modern auditory features like TRILL vectors on CNN show high accuracy (81.86%) in stress classification.

Stress Detection based on TEO and MFCC speech features using Convolutional Neural Networks (CNN)

Stress detection through speech is superior due to its non-invasive nature. The fusion of TEO and MFCC features, analyzed by CNN, achieved high accuracies of 95.83% for males and 95.37% for females.

Stress Detection based on TEO and MFCC speech features using Convolutional Neural Networks (CNN)

Stress detection through speech is advantageous due to its non-invasive nature. The fusion of TEO and MFCC features, analyzed by CNN, achieved high accuracies for recognizing stressed emotions.