Towards an automatic evaluation of the dysarthria level of patients with Parkinson's disease

Background: Parkinson's disease (PD) is a neurological disorder that produces motor and non motor impairments. The evaluation of motor symptoms is currently performed following the third section of the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS-III); however, only one item of that scale is related to speech impairments. It is necessary to develop a specific scale such that considers those aspects related to speech impairments of the patients. Aims: (i) To introduce and evaluate the suitability of a modified version of the Frenchay Dysarthria Assessment (m-FDA) scale to quantify the dysarthria level of PD patients; (ii) to objectively model dysarthric speech signals considering four speech dimensions; (iii) to develop a methodology, based on speech processing and machine learning methods, to automatically quantify/predict the dysarthria level of patients with PD. Methods: The speech recordings are modeled using features extracted from several dimensions of speech: phonation, articulation, prosody, and intelligibility. The dysarthria level is quantified using linear and non-linear regression models. Speaker models based on i-vectors are also explored. Results and conclusions: The m-FDA scale was introduced to assess the dysarthria level of patients with PD. Articulation features extracted from continuous speech signals to create i-vectors were the most accurate to quantify the dysarthria level, with correlations of up to 0.69 between the predicted m-FDA scores and those assigned by the phoniatricians. When the dysarthria levels were estimated considering dedicated speech exercises such as rapid repetition of syllables (DDKs) and read texts, the correlations were 0.64 and 0.57, respectively. In addition, the combination of several feature sets and speech tasks improved the results, which validates the hypothesis about the contribution of information from different tasks and feature sets when assessing dysarthric speech signals. The speaker models seem to be promising to perform individual modeling for monitoring the dysarthria level of PD patients. The proposed approach may help clinicians to make more accurate and timely decisions about the evaluation and therapy associated to the dysarthria level of patients. The proposed approach is a great step towards unobtrusive/ecological evaluations of patients with dysarthric speech without the need of attending medical appointments.