# Prosody-Based Measures for Automatic Severity Assessment of Dysarthrie Speech
## 1 Introduction
The current study proposes measures related to prosody, as prosodic impairments are one of the earliest cues of dysarthria [28]. The study in Reference [28] examines the speech of 23 individuals with untreated early-stage Parkinson's, where 18 of the 23 are found to have dysarthria. Among the 18 individuals with dysarthria, phonatory deficits are present in 6 cases (26.09%), articulatory issues in 9 cases (39.13%), and 14 cases with prosody-based impairments (60.87%). Similarly, in Reference [29], features related to phonation (glottis features), articulation (MFCCs), and prosody (F0, energy, duration, pauses, jitter, and shimmer) are extracted from both healthy and dysarthric speakers with early-stage Parkinson's. These acoustic features are used as input to a support vector machine (SVM) classifier to predict whether a given utterance is from a healthy speaker or a speaker with dysarthria. Results show that training on prosodic features reaches an accuracy of 90.5%, while classifiers trained on glottal and articulatory features achieve an accuracy of 78.6% and 88.1%, respectively. Results from References [28; 29] suggest that prosodic impairments are prevalent in dysarthric speech and prosody-based measures can be useful for the assessment of dysarthric speech. The most salient prosodic impairments in dysarthric speech are related to pitch and speech rate. In Reference [30], the length of tone units, fundamental frequency (F0), and standard deviation of fundamental frequency from spontaneous speech are collected from healthy and dysarthric speakers. Speech from speakers with severe dysarthria displays shorter tone units and higher mean F0 compared to mild dysarthria and healthy controls. Patients with mild dysarthria have lower standard deviations of F0 (more monotonous speech) than healthy controls and severe dysarthric speakers. The findings in Reference [30] are further supported by later studies in speakers with multiple sclerosis, cerebral disease, and motor neuron disease [31; 32]. Research with Korean speakers reports similar prosodic impairments in dysarthric speakers [33; 34; 35]. In Reference [33], an acoustic analysis of healthy speakers and speakers with dysarthria reveals longer syllable duration, varied pitch range, and more frequent pauses among speakers with dysarthria. Similarly, results from Reference [34] suggest that dysarthric speech has lower pitch values for interrogative sentences. Lastly, the prosodic characteristics of speakers with dysarthria from a range of neurological disorders are analyzed in Reference [35]. Results indicate that dysarthric speech has a reduced speaking rate, reduced articulation rate, and reduced F0 slope compared to healthy speech. Previous studies, such as in References [23; 29], suggest that the use of prosody with machine learning methods can accurately classify healthy speech from dysarthric speech. In Reference [23], voice quality features such as HNR, shimmer, and jitter measures are extracted along with a prosody feature set, which includes F0 measures, utterance, and phone duration. A linear discriminant analysis (LDA)-based classifier is used to achieve an accuracy of 71.9% and 82.1% for voice quality and prosody feature sets, respectively. These previously mentioned studies take a binary approach to dysarthric speech detection and do not consider severity. However, in Reference [36], 11 prosodic features are used to automatically assess the severity level of dysarthric speakers from the publicly available Neymours database [37]. An LDA based feature selection method is used to determine the most discriminative prosodic features as follows (from the most to the least discriminative): articulation rate, number of pitch periods, mean pitch, voice breaks, %V, HNR, jitter, shimmer, standard deviation of pitch, standard deviation of pitch period, and NHR. These features can assess four levels of dysarthric speech with an accuracy of 88.89% when using a Gaussian mixture model classifier, and an accuracy of 93% with an SVM classifier.