Abstract:
Mental stress persisting for long can cause severe health issues. There are various approaches available in the literature for investigating stress through speech utterances. The available procedure to obtain speech under stress dataset requires the speakers to undergo the actual stress situations in a real environment with limited control or inducing stress with a mental task in a lab environment. These approaches either suffer from ethical issues or unreliable labeling of the obtained speech samples. In this paper, we attempt to overcome these limitations with Induced mental Stress based speech production And labeling Procedure (ISAP), for obtaining speech utterances under mental stress along with labeling the samples simultaneously. The proposed ISAP can be incorporated by future studies as per their need to create a speech under stress dataset. We also present the obtained dataset, the baseline experiments, and classification results with various machine learning models. A total of 1260 speech utterances are obtained, with ISAP able to induce stress in 54.4% of the cases. The accuracy of the SVM classifier in recognizing three stress classes, namely, No Stress, Low Stress, and High Stress is found to be 57.1%.