Assessing stress levels via speech using three reading patterns

Various problems caused by stress seriously affect individuals' physical and mental well-being and have been receiving an increasing attention in modern lives. Since traditional stress assessment methods are lack of objectivity, affective sensing technologies have been studied for years. As detecting stress in speech has the advantages of non-invasive, portable, fast, and less expensive, many explorations were conducted to build stress assessment models. To find out a proper acoustic feature subset for a specific reading pattern, we performed the experiments with 30 subjects by three reading patterns: vowel, figure and sentence. We utilized feature selection and classification techniques to automatically select acoustic features and evaluate performances. Results showed that there are interactions between reading patterns and stress levels on speech features. Although Stress levels can be distinguished in any pattern of them (vowel, figure, sentence), sentence is a better choice with the best classification accuracy 88.15%. Furthermore, Line Spectral Pairs (LSP) features are indispensable for vowel, Mel-Frequency Cepstral Coefficient (MFCC) features are more effective for figure and the combination of prosodic, LSP and MFCC features is more suitable for sentence.