Multi-Scale Spatial and Temporal Speech Associations to Swallowing for Dysphagia Screening

Abstract

Dysphagia is a common symptom of many neurological diseases. It often occurs in older adults and increases the risk of aspiration pneumonia. Existing diagnosis systems of dysphagia are invasive or require patients to swallow liquids, which are costly and harmful to the patients. In this work, we propose an early screening system of dysphagia based on two kinds of throat signals, i.e., vowels and sentences. Based on the vowels, two new categories of speech features are developed: PET (pitch/energy trajectory) and FS-Conts (full spectrogram contours). The PET feature set focuses on the prominent resonance energy of speech to track the pitch and energy fluctuations. It can reflect the stability of vocal cords in the speech generation process. The FS-Conts feature set is proposed to emphasize the spatial details of formants based on three-dimensional contours. Concerning the sentences, three categories of speech features are proposed, called LSSDL (log symmetric spectral difference level), C-coes (crucial energy coefficients), and LDF (local dynamic features). The three features explore the speech representations of dysphagia from global variations to local associations. The LSSDL feature set is designed to highlight the global spectral differences in the interested frequency region. The C-coes and LDF feature sets locate local speech differences in specific frequency regions and time duration. In addition, a new feature selection algorithm is developed based on a newly designed precise matching analysis technique to search for distinguishing features. In the classification experiments, the SVM classifier is adopted and the dysphagia detection accuracy reaches 95.07%. The comparative experiments are conducted. The results indicate that our system performs better than the existing methods.