Early onset ataxia represents a group of heterogeneous neurological conditions typically characterized by motor disability. Speech problems are one of the main core features of ataxic syndromes, where automatic and computational characterization of speech impairment might represent a source of biomarkers for early screening and stratification of patients. The main contribution of this paper consists in proposing a novel hierarchical machine learning model (HMLM) to improve detection and assessment of dysarthria from a structured speech disturbance test. Performances are tested on a new audio dataset containing 10 seconds recordings of standardized clinical PATA test for 55 subjects: 18 healthy subjects and 37 with ataxia. Results show that the proposed HMLM achieves performances with an accuracy of about 90% at the first level (healthy vs patients) selecting an optimal subset of conventional features. In cascade, at the second level, speech disturbance severity (Low vs High) is assessed using deep learning feature extraction technique based on a VGG pre-trained network with maximum accuracy of about 80%. Both levels are processed through the majority voting ensemble technique testing Support Vector Machine (SVM), k-Nearest Neighbors (kNN), Decision Tree (DT) and Naïve Bayes (NB). In our results, the use of HMLM considerably outperforms the results achieved with a single machine learning or deep learning modeling. These outcomes demonstrate that the investigation of the PATA speech test through HMLM can be considered very promising. We also observed that the use of conventional feature extraction techniques and machine learning modeling seems to be a good solution for the diagnosis of patients with ataxia, while the deep learning approach is more appropriate for stratification of severity of dysarthria.
