Using X-Vectors To Automatically Detect Parkinson''s Disease From Speech

The promise of new neuroprotective treatments to stop or slow the advance of Parkinson's Disease (PD) urges for new biomarkers or detection schemes that can deliver a faster diagnosis. Given that speech is affected by PD, the combination of deep neural networks and speech processing can provide automatic detection schemes. Accordingly, in this study we analyze for the first time a new state-of-the-art speaker recognition technique, x-Vectors, in a different scenario: the automatic detection of PD from speech. The proposed approach is compared with another speaker recognition technique, i-Vectors, employed in previous works and used as baseline in this study. A corpus with 43 PD patients and 46 control speakers was used to evaluate the performance of these two techniques at two sampling frequencies: 8 and 16 kHz. The x-Vector approach provided the best results in terms of accuracy and AUC reaching values of 90% and 0.94, respectively. Consequently, results suggest that speaker embeddings obtained using deep neural networks are successful extracting acoustic information relative to patterns in articulation, prosody and/or phonation common in persons with PD.