# Prosody-Based Measures for Automatic Severity Assessment of Dysarthrie Speech
## 1 Introduction  
Neurological disorders come with a range of cognitive and physical issues that can make life difficult for the affected individual. Speech is one aspect that can be severely damaged and lead to issues in communication. A common speech disorder known as dysarthria often occurs in individuals with a variety of neurological disorders such as Cerebral Palsy, Parkinson's Disease, Amyotrophic Lateral Sclerosis (ALS), Multiple Sclerosis, among others. Research indicates that dysarthria occurs up to 90% of the time in patients with Parkinson's Disease [1], and 50% of the time for individuals with Multiple Sclerosis [2]. Dysarthria is also one of the first symptoms of ALS in 25% of patients [3]. Dysarthria can also co-occur with aphasia in patients who suffered from acute stroke. The study in Reference [4] reports both dysarthria and aphasia in 10% of post-stroke patients, while in Reference [5], the co-occurrence reaches 29.6%. In Korean speakers, both dysarthria and aphasia are present in 35.4% of 150 post-acute stroke patients [6]. Dysarthria also occurs with non-fluent or agrammatic aphasia [7; 8]. Researchers report dysprosody and a slower speech rate pose a prominent impairment in some patients with agrammatic aphasia [9; 10]. Typically, dysarthria is diagnosed by a trained speech pathologist who administers several speaking tasks to the patient in order to perceptually evaluate any speech irregularities [11; 12]. For example, one can determine impaired prosody byhaving patients read sentences and observe any irregular variations in pitch, duration, or stress. Several standardized assessments based on perceptual evaluation have been proposed, with the Mayo Clinic Rating System [13] and Frenchay Dysarthria Assessment being the most detailed and commonly utilized tests for English speakers [14]. Despite the wide use of perceptual evaluation, the subjective nature of the task and excessively long duration of administering these tests are common criticisms. Furthermore, low identification accuracy is reported in Reference [15], while low intra- and inter-rater reliability are reported in Reference [16] and Reference [17] for the Mayo Clinic Rating System. The prevalence of machine learning, especially deep learning, for speech and audio classification has introduced a variety of methods for automatically detecting and even assessing the severity of dysarthric speech. Detecting dysarthria involves extracting hand-crafted acoustic features and using those features as inputs to a machine learning-based classifier [18; 19; 20]. Deep learning approaches are also possible where the raw speech signal or a set of elementary features are fed into complex neural network architectures that automatically determine the important acoustic information and distinguish between healthy and dysarthric speech [21; 22]. Deep learning approaches require less data preparation and feature engineering but may suffer from a lack of interpretability as further post-processing is often required to interpret how the speaker's speech is impaired. Another issue with the deep learning approach for impaired speech detection is the lack of data. The success of deep learning has largely been the result of big data and the ability to train on large datasets. Unfortunately, the collection of impaired speech data is difficult and available datasets are very limited. Various types of acoustic features have been proposed for detecting dysarthric speech. Spectral features such as Mel Frequency Cepstral Coefficients (MFCCs) are used in References [22; 23], and filter banks are utilized in long short-term memory classifiers [21] and convolutional neural networks [24]. Spectral measures of fricatives are shown to significantly differ between healthy and dysarthric speakers in Reference [25] and are used as input to a machine learning classifier in Reference [26]. Results from Reference [27] indicate that glottal features can improve detection performance against a baseline OpenSmile acoustic feature set.