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In this study, we focus on the classification of neutral and stressed speech based on a physical model. In order to represent the characteristics of the vocal folds and vocal tract during the process of speech production and to explore the physical parameters involved, we propose a method using the two-mass model. As feature parameters, we focus on stiffness parameters of the vocal folds, vocal tract length, and cross-sectional areas of the vocal tract. The stiffness parameters and the area of the entrance to the vocal tract are extracted from the two-mass model after we fit the model to real data using our proposed algorithm. These parameters are related to the velocity of glottal airflow and acoustic interaction between the vocal folds and the vocal tract and can precisely represent features of speech under stress because they are affected by the speaker’s psychological state during speech production. In our experiments, the physical features generated using the proposed approach are compared with traditionally used features, and the results demonstrate a clear improvement of up to 10% to 15% in average stress classification performance, which shows that our proposed method is more effective than conventional methods.

Classification of speech under stress based on modeling of the vocal folds and vocal tract