Abstract
Dysarthria frequently occurs in individuals with disorders such as stroke, Parkinson's disease, cerebral palsy, and other neurological disorders. Well-timed detection and management of dysarthria in these patients is imperative for efficiently handling the development of their condition. Several previous studies have concentrated on detecting dysarthria speech using machine learning-based methods. However, the false positive rate is high due to the varying nature of speech and environmental factors such as background noise. Therefore, in this work, we employ a model based on the Swin transformer (ST), namely DSR-Swinoid. Firstly, the speech is converted into mel-spectrograms to reflect the maximum patterns of voice signals. Despite the ST's initial aim to effectively extract the local and global visual features, it still prioritizes global features. Meanwhile, in mel-spectrograms, the specific gaps due to slurred speech are considered. Therefore, our objective is to improve the ST's capacity for learning local features by introducing 4 modules: network for local feature capturing (NLF), convolutional patch concatenation, multi-path (MP), and multi-view block (MVB). The NLF module enriches the existing Swin transformer by enhancing its capability to capture local features effectively. MP integrates features from different Swin phases to emphasize local information. In the meantime, the MVB-ST block surpasses classical Swin blocks by integrating diverse receptive fields, focusing on a more comprehensive extraction of local features. Investigational outcomes explain that the DSR-Swinoid attains the best exactness of 98.66%, exceeding the outcomes by existing methods.