Automatic Detection of Allergic Rhinitis in Patients

This paper presents a system to detect symptoms of allergic rhinitis remotely by using uttered speech and by exploiting its specific spectral characteristics. Based on the principles of adaptive modelling and fundamental frequency variations (jitter) as well as speech analysis by means of acoustic models, the proposed technique achieves an efficient classification of patients from uttered speech. A Singular Value Decomposition based iterative approach is used for the accurate estimation of the jitter and Hidden Markov Models are implemented to model the 32 phonemes. The final decision is derived by optimally combining the individual estimates providing a tool for the automatic diagnosis of allergic rhinitis.