A Survey on The Impact of Early Speech and Vocal Changes on Young Onset Parkinson’s Disease.

Patients with Young Onset Parkinson's Disease may have a long medical journey to receive their diagnosis due to their atypical age. Parkinson's Disease is typically diagnosed in a geriatric population and thus assumed to be a late-onset neurodegenerative disorder. Therefore, when younger people approach clinicians with parkinsonian symptoms, they are typically overlooked as they do not meet the age criteria, and thus the diagnosis may be missed or delayed. In late-onset Parkinson's Disease, a classic primary symptom pertains to voice and speech disorders due to the high prevalence of hypokinetic dysarthria. Thus, a review of speech and voice deficits that are seen prior to or within the time frame of diagnosis can highlight the speech and vocal patterns clinicians may see within a younger population. This could provide an effective tool for clinicians to make a quicker diagnosis for patients and administer medication such as Levodopa without having the patient go through rigorous, time-consuming testing. Furthermore, within neuroscience, little attention is paid to the impact of early speech and vocal changes. Therefore, this study would also like to explore the impact of these changes, highlighting the urge for clinicians not to stigmatise younger patients by age to receive a rapid diagnosis and treatment. This study follows the proceedings of a survey methodology via a formulated questionnaireinserted in a Google Form containing 12 statements, which contained closed-ended questions (Yes/No indicators) and open-ended questions where the participants indicated their answer by filling in a short statement regarding their experience. The statements contained questions about the diagnosis of Parkinson's Disease, the speech and vocal changes experienced, the socio-social effects of the speech and vocal changes on their personal lives and if they found that medication helped their vocal and speech symptoms. The questionnaire yielded a total of 43 participants with young-onset Parkinson's Disease. The results indicated that most of the participants suffered from speech and vocal changes, which resembled the clinical profile of severe hypokinetic dysarthria, typically seen in later stages of Parkinson's Disease in late-onset. In addition, the changes in speech and voice were so impactful that they caused significant distress in the psychosocial domain of their lives. Despite the severity of the speech and vocal changes, most participants struggled to receive a diagnosis, while hardly any received appropriate speech therapy treatment to aid their overall quality of life. Thus, this study concludes that the results of this study are essential to break stigmas and open the conversation in neuroscience and neurology on YOPD. Improvement clinical knowledge of this unique subtype of Parkinson's needs to be stressed amongst clinical practitioners that age of onset does not play a role in managing, treating, and diagnosing Parkinson's Disease.

A Neuromotor to Acoustical Jaw-Tongue Projection Model With Application in Parkinson’s Disease Hypokinetic Dysarthria

AimThe present work proposes the study of the neuromotor activity of the masseter-jaw-tongue articulation during diadochokinetic exercising to establish functional statistical relationships between surface Electromyography (sEMG), 3D Accelerometry (3DAcc), and acoustic features extracted from the speech signal, with the aim of characterizing Hypokinetic Dysarthria (HD). A database of multi-trait signals of recordings from an age-matched control and PD participants are used in the experimental study.Hypothesis:The main assumption is that information between sEMG and 3D acceleration, and acoustic features may be quantified using linear regression methods.MethodsRecordings from a cohort of eight age-matched control participants (4 males, 4 females) and eight PD participants (4 males, 4 females) were collected during the utterance of a diadochokinetic exercise (the fast repetition of diphthong [aI]). The dynamic and acoustic absolute kinematic velocities produced during the exercises were estimated by acoustic filter inversion and numerical integration and differentiation of the speech signal. The amplitude distributions of the absolute kinematic and acoustic velocities (AKV and AFV) are estimated to allow comparisons in terms of Mutual Information.ResultsThe regression results show the relationships between sEMG and dynamic and acoustic estimates. The projection methodology may help in understanding the basic neuromotor muscle activity regarding neurodegenerative speech in remote monitoring neuromotor and neurocognitive diseases using speech as the vehicular tool, and in the study of other speech-related disorders. The study also showed strong and significant cross-correlations between articulation kinematics, both for the control and the PD cohorts. The absolute kinematic variables presents an observable difference for the PD participants compared to the control group.ConclusionKinematic distributions derived from acoustic analysis may be useful biomarkers toward characterizing HD in neuromotor disorders providing new insights into PD.

Comparing Ensemble-Based Machine Learning Classifiers Developed for Distinguishing Hypokinetic Dysarthria from Presbyphonia

It is essential to understand the voice characteristics in the normal aging process to accurately distinguish presbyphonia from neurological voice disorders. This study developed the best ensemble-based machine learning classifier that could distinguish hypokinetic dysarthria from presbyphonia using classification and regression tree (CART), random forest, gradient boosting algorithm (GBM), and XGBoost and compared the prediction performance of models. The subjects of this study were 76 elderly patients diagnosed with hypokinetic dysarthria and 174 patients with presbyopia. This study developed prediction models for distinguishing hypokinetic dysarthria from presbyphonia by using CART, GBM, XGBoost, and random forest and compared the accuracy, sensitivity, and specificity of the development models to identify the prediction performance of them. The results of this study showed that random forest had the best prediction performance when it was tested with the test dataset (accuracy = 0.83, sensitivity = 0.90, and specificity = 0.80, and area under the curve (AUC) = 0.85). The main predictors for detecting hypokinetic dysarthria were Cepstral peak prominence (CPP), jitter, shimmer, L/H ratio, L/H ratio_SD, CPP max (dB), CPP min (dB), and CPPF0 in the order of magnitude. Among them, CPP was the most important predictor for identifying hypokinetic dysarthria.

Comparing Lexical Cues in Listener Processing of Dysarthria and Speech in Noise

Purpose The frequency of a word and its number of phonologically similar neighbors can dramatically affect how likely it is to be accurately identified in adverse listening conditions. This study compares how these two cues affect listeners' processing of speech in noise and dysarthric speech. Method Seven speakers with moderate hypokinetic dysarthria and eight healthy control speakers were recorded producing the same set of phrases. Statements from control speakers were mixed with noise at a level selected to match the intelligibility range of the speakers with dysarthria. A binomial mixed-effects model quantified the effects of word frequency and phonological density on word identification. Results The model revealed significant effects of word frequency ( b = 0.37, SE = 0.12, p = .002) and phonological neighborhood density ( b = 0.40, SE = 0.12, p = .001). There was no effect of speaking condition (i.e., dysarthric speech vs. speech in noise). However, a significant interaction was observed between speaking condition and word frequency ( b = 0.26, SE = 0.04, p < .001). Conclusions The model's interactions indicated that listeners were more strongly influenced by the effects of word frequency when decoding moderate hypokinetic dysarthria as compared to speech in noise. Differences in listener reliance on lexical cues may have important implications for the selection of communication-based treatment strategies for speakers with dysarthria.

Speech and language therapy treatment on hypokinetic dysarthria in Parkinson disease: Systematic review and meta-analysis

Objective: To assess the effect of speech and language therapy (SLT) on Hypokinetic dysarthria (HD) in Parkinson’s disease. Design: Systematic review and meta-analysis of randomized controlled trials. Methods: We performed a literature search of randomized controlled trials using PubMed, Web of Science, Science Direct and Cochrane database (last search October 2020). Quality assessment and risk of bias were assessed using the Downs and Black scale and the Cochrane tool. The data were pooled and a meta-analysis was completed for sound pressure levels, perceptual intelligibility and inflection of voice fundamental frequency. Results: We selected 15 high to moderate quality studies, which included 619 patients with Parkinson’s disease. After pooling the data, 7 studies, which compared different speech language therapies to no treatment, control groups and 3 of their variables, (sound pressure level, semitone standard deviation and perceptual intelligibility) were included in the analysis. Results showed significant differences in favor of SLT for sound pressure level sustained phonation tasks (standard mean difference = 1.79; 95% confidence interval = 0.86, 2.72; p ⩽ 0.0001). Significant results were also observed for sound pressure level and semitone standard deviation in reading tasks (standard mean difference = 1.32; 95% confidence interval = 1.03, 1.61; p ⩽ 0.0001). Additionally, sound pressure levels in monologue tasks showed similar results when SLT was compared to other treatments (standard mean difference = 0.87; 95% confidence interval = 0.46, 1.28; p ⩽ 0.0001). Conclusion: This meta-analysis suggests a beneficial effect of SLT for reducing Hypokinetic Dysarthria in Parkinson’s disease, improving perceptual intelligibility, sound pressure level and semitone standard deviation.

Using Crowdsourced Listeners' Ratings to Measure Speech Changes in Hypokinetic Dysarthria: A Proof-of-Concept Study

Purpose Interventions for speech disorders aim to produce changes that are not only acoustically measurable or perceptible to trained professionals but are also apparent to naive listeners. Due to challenges associated with obtaining ratings from suitably large listener samples, however, few studies currently evaluate speech interventions by this criterion. Online crowdsourcing technologies could enhance the measurement of intervention effects by making it easier to obtain real-world listeners' ratings. Method Stimuli, drawn from a published study by Sapir et al. (“Effects of intensive voice treatment (Lee Silverman Voice Treatment [LSVT]) on vowel articulation in dysarthric individuals with idiopathic Parkinson disease: Acoustic and perceptual findings” in Journal of Speech, Language, and Hearing Research, 50 (4), 2007), were words produced by individuals who received intensive treatment (LSVT LOUD) for hypokinetic dysarthria secondary to Parkinson's disease. Thirty-six online naive listeners heard randomly ordered pairs of words elicited pre- and posttreatment and reported which they perceived as “more clearly articulated.” Results Mixed-effects logistic regression indicated that words elicited posttreatment were significantly more likely to be rated “more clear.” Across individuals, acoustically measured magnitude of change was significantly correlated with pre–post difference in listener ratings. Conclusions These results partly replicate the findings of Sapir et al. (2007) and demonstrate that their acoustically measured changes are detectable by everyday listeners. This supports the viability of using crowdsourcing to obtain more functionally relevant measures of change in clinical speech samples. Supplemental Material https://doi.org/10.23641/asha.12170112