Multi-Band Speech Tensor Decomposition for Interactive Feature Extraction in Early Dysphagia Screening

Dysphagia is a prevalent symptom in numerous neurological disorders among older adults. Current dysphagia diagnostic systems either involve invasive procedures or necessitate the ingestion of liquids. Some researchers have devised automatic dysphagia detection methods based on vowels that are easy to collect and sensitive to vocal cord states. These methods extract features from each vowel separately and fuse them to train models. Nonetheless, they neglect potential interrelations among different vowels. Vowels collected from the same speaker could share subspaces since they are produced from the same vocal system. In this study, we introduce a tensor-based method that can simultaneously extract interactive information from all vowels across different modes. This method designs multi-band speech tensors and core-pruned tensor networks to investigate crucial frequency bands and connections for dysphagia screening. Experimental results show our model exceeds previous methods by approximately 10 percentage points in the classification accuracy.