Detection of the common cold from speech signals using transformer model and spectral features

The acoustic and prosodic characteristics of speech exhibit alterations when individuals are affected by different health conditions. The field of biomedical engineering holds significant potential in the advancement of non-invasive diagnostic systems that utilize voice as a modality. The common cold is an infectious sickness that affects a large number of people all over the world each year. This paper presents the utilization of various spectral features and a transformer-based model with focal loss function for classifying cold-affected and healthy speech signals. A spectral feature consisting of Mel frequency cepstral coefficients (MFCC), Mel-spectrogram, chromagram, spectral contrast, spectral centroid, spectral bandwidth, spectral flatness, and spectral roll-off features. The efficacy of the proposed methodology is assessed using the URTIC database. The findings indicate that the proposed framework has better results compared to existing state-of-the-art approaches. We have achieved the UAR of 69.55% on the develop set and 70.48% on the test set of the URTIC database. These preliminary findings exhibit significant potential for future investigation in this domain.