Automatic detection of the upper respiratory tract infection from speech using intrinsic mode multi-domain feature fusion

Creating non-invasive diagnosis procedures using speech is a very promising research area in biomedical engineering. Screening for an upper respiratory tract infection (URTI) or common cold using speech signals may be advantageous in terms of preventing its spread. In this study, we have proposed the various intrinsic mode multi-domain feature fusions for diagnosing the URTIs. First, each active speech frame is decomposed into several intrinsic mode functions (IMFs) using variational mode decomposition to capture the non-linear and non-stationary characteristics of pathological speech. Then spectral and entropy domain features are extracted from each IMF and used as features for classification. The URTIC and the newly recorded PRECRU URTIS database are employed to evaluate the efficacy of the proposed features. A transformer-based framework is employed to evaluate the discriminative capacity of the features, focusing on their capability to simulate long-range relationships with features. The proposed features outperform state-of-the-art methods, achieving a UAR of 70.82% and 72.24%, respectively, on the URTIC database's development and test partitions and 76.06% on the PREC-RU URTIS database. The results emphasize the efficacy of integrating intrinsic mode spectral and entropy features for reliable URTI identification