The purpose of this study was to analyze vowel articulation across various speaking tasks in a group of 20 early Parkinson's disease (PD) individuals prior to pharmacotherapy. Vowels were extracted from sustained phonation, sentence repetition, reading passage, and monologue. Acoustic analysis was based upon measures of the first (F1) and second (F2) formant of the vowels /a/, /i/, and /u/, vowel space area (VSA), F2i/F2u and vowel articulation index (VAI). Parkinsonian speakers manifested abnormalities in vowel articulation across F2u, VSA, F2i/F2u, and VAI in all speaking tasks except sustained phonation, compared to 15 age-matched healthy control participants. Findings suggest that sustained phonation is an inappropriate task to investigate vowel articulation in early PD. In contrast, monologue was the most sensitive in differentiating between controls and PD patients, with classification accuracy up to 80%. Measurements of vowel articulation were able to capture even minor abnormalities in speech of PD patients with no perceptible dysarthria. In conclusion, impaired vowel articulation may be considered as a possible early marker of PD. A certain type of speaking task can exert significant influence on vowel articulation. Specifically, complex tasks such as monologue are more likely to elicit articulatory deficits in parkinsonian speech, compared to other speaking tasks.

For generations, the evaluation of speech abnormalities in neurodegenerative disorders such as Parkinson’s disease (PD) has been limited to perceptual tests or user-controlled laboratory analysis based upon rather small samples of human vocalizations. Our study introduces a fully automated method that yields significant features related to respiratory deficits, dysphonia, imprecise articulation and dysrhythmia from acoustic microphone data of natural connected speech for predicting early and distinctive patterns of neurodegeneration. We compared speech recordings of 50 subjects with rapid eye movement sleep behaviour disorder (RBD), 30 newly diagnosed, untreated PD patients and 50 healthy controls, and showed that subliminal parkinsonian speech deficits can be reliably captured even in RBD patients, which are at high risk of developing PD or other synucleinopathies. Thus, automated vocal analysis should soon be able to contribute to screening and diagnostic procedures for prodromal parkinsonian neurodegeneration in natural environments.

Most patients with movement disorders have speech impairments resulting from sensorimotor abnormalities that affect phonatory, articulatory, and prosodic speech subsystems. There is widespread cross‐discipline use of speech recordings for diagnostic and research purposes, despite which there are no specific guidelines for a standardized method. This review aims to combine the specific clinical presentations of patients with movement disorders, existing acoustic assessment protocols, and technological advances in capturing speech to provide a basis for future research in this field and to improve the consistency of clinical assessments. We considered 3 areas: the recording environment (room, seating, background noise), the recording process (instrumentation, vocal tasks, elicitation of speech samples), and the acoustic outcome data. Four vocal tasks, namely, sustained vowel, sequential and alternating motion rates, reading passage, and monologues, are integral aspects of motor speech assessment. Fourteen acoustic vocal speech features, including their hypothesized pathomechanisms with regard to typical occurrences in hypokinetic or hyperkinetic dysarthria, are hereby recommended for quantitative exploratory analysis. Using these acoustic features and experimental speech data, we demonstrated that the hyperkinetic dysarthria group had more affected speech dimensions compared with the healthy controls than had the hypokinetic speakers. Several contrasting speech patterns between both dysarthrias were also found. This article is the first attempt to provide initial recommendations for a standardized way of recording the voice and speech of patients with hypokinetic or hyperkinetic dysarthria; thus allowing clinicians and researchers to reliably collect, acoustically analyze, and compare vocal data across different centers and patient cohorts. © 2020 International Parkinson and Movement Disorder Society

Although speech disorder is frequently an early and prominent clinical feature of Parkinson's disease (PD) as well as atypical parkinsonian syndromes (APS) such as progressive supranuclear palsy (PSP) and multiple system atrophy (MSA), there is a lack of objective and quantitative evidence to verify whether any specific speech characteristics allow differentiation between PD, PSP and MSA. Speech samples were acquired from 77 subjects including 15 PD, 12 PSP, 13 MSA and 37 healthy controls. The accurate differential diagnosis of dysarthria subtypes was based on the quantitative acoustic analysis of 16 speech dimensions. Dysarthria was uniformly present in all parkinsonian patients but was more severe in PSP and MSA than in PD. Whilst PD speakers manifested pure hypokinetic dysarthria, ataxic components were more affected in MSA whilst PSP subjects demonstrated severe deficits in hypokinetic and spastic elements of dysarthria. Dysarthria in PSP was dominated by increased dysfluency, decreased slow rate, inappropriate silences, deficits in vowel articulation and harsh voice quality whereas MSA by pitch fluctuations, excess intensity variations, prolonged phonemes, vocal tremor and strained-strangled voice quality. Objective speech measurements were able to discriminate between APS and PD with 95% accuracy and between PSP and MSA with 75% accuracy. Dysarthria severity in APS was related to overall disease severity (r = 0.54, p = 0.006). Dysarthria with various combinations of hypokinetic, spastic and ataxic components reflects differing pathophysiology in PD, PSP and MSA. Thus, motor speech examination may provide useful information in the evaluation of these diseases with similar manifestations.

This multilanguage study used simple speech recording and high-end pattern analysis to provide sensitive and reliable noninvasive biomarkers of prodromal versus manifest α-synucleinopathy in patients with idiopathic rapid eye movement sleep behavior disorder (iRBD) and early-stage Parkinson disease (PD). Methods: We performed a multicenter study across the Czech, English, German, French, and Italian languages at 7 centers in Europe and North America. A total of 448 participants (337 males), including 150 with iRBD (mean duration of iRBD across language groups 0.5-3.4 years), 149 with PD (mean duration of disease across language groups 1.7-2.5 years), and 149 healthy controls were recorded; 350 of the participants completed the 12-month follow-up. We developed a fully automated acoustic quantitative assessment approach for the 7 distinctive patterns of hypokinetic dysarthria. Results: No differences in language that impacted clinical parkinsonian phenotypes were found. Compared with the controls, we found significant abnormalities of an overall acoustic speech severity measure via composite dysarthria index for both iRBD (p = 0.002) and PD (p < 0.001). However, only PD (p < 0.001) was perceptually distinct in a blinded subjective analysis. We found significant group differences between PD and controls for monopitch (p < 0.001), prolonged pauses (p < 0.001), and imprecise consonants (p = 0.03); only monopitch was able to differentiate iRBD patients from controls (p = 0.004). At the 12-month follow-up, a slight progression of overall acoustic speech impairment was noted for the iRBD (p = 0.04) and PD (p = 0.03) groups. Interpretation: Automated speech analysis might provide a useful additional biomarker of parkinsonism for the assessment of disease progression and therapeutic interventions.