A machine-learning model fusing acoustic and semantic signals detected daily stress from real-life speech with 81.20% accuracy and 87.46% F1-score, improving upon acoustic signals alone.
Machine learning models fusing acoustic and semantic signals from real-life speech can accurately detect daily stress, performing close to human recognition capability.
Absolute Event Rate: 81.2% vs 74.25%
Detecting daily stress is of vital importance for workplace safety and health, and natural speech is recommended as one of the main methods of mental stress detection. This study developed machine-learning models for daily stress detection from real-life speeches by fusing its acoustic and semantic signals. First, we collected real-life speech data from life-stress-catharsis room of online chat platform and established a speech database with real daily stress. Second, we obtained the model performances of common machine-learning classifiers for stress detection and compared them with human performance. The stress-detection classifiers achieved a promising performance of 74.25% accuracy and 83.73% F1-score using only acoustic signal. By fusing with the semantic signal, the stress detection model performance was significantly improved and achieved a performance of 81.20% accuracy and 87.46% F1-score, which validated the importance of semantic information in daily stress detection. Meanwhile, the best performance of the machine learning model was close to the human recognition capability. The results of this study validated the feasibility of detecting daily stress based on real speech. The models developed in this study could be used for daily stress detection in real life and can provide information for stress interventions to ease the negative effects on health.
Lu et al. (Mon,) conducted a other in Daily stress. Machine-learning models fusing acoustic and semantic signals vs. Acoustic signal only was evaluated on Stress detection accuracy. A machine-learning model fusing acoustic and semantic signals detected daily stress from real-life speech with 81.20% accuracy and 87.46% F1-score, improving upon acoustic signals alone.
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