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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.

IntroductionEarly identification of Parkinson’s disease (PD) in its prodromal stage has fundamental implications for the future development of neuroprotective therapies. However, no sufficiently accurate biomarkers of prodromal PD are currently available to facilitate early identification. The vocal assessment of patients with isolated rapid eye movement sleep behaviour disorder (iRBD) and PD appears to have intriguing potential as a diagnostic and progressive biomarker of PD and related synucleinopathies.Methods and analysisSpeech patterns in the spontaneous speech of iRBD, early PD and control participants’ voice calls will be collected from data acquired via a developed smartphone application over a period of 2 years. A significant increase in several aspects of PD-related speech disorders is expected, and is anticipated to reflect the underlying neurodegeneration processes.Ethics and disseminationThe study has been approved by the Ethics Committee of the General University Hospital in Prague, Czech Republic and all the participants will provide written, informed consent prior to their inclusion in the research. The application satisfies the General Data Protection Regulation law requirements of the European Union. The study findings will be published in peer-reviewed journals and presented at international scientific conferences.

Although motor speech disorders represent an early and prominent clinical feature of multiple system atrophy (MSA), the potential usefulness of speech assessment as a diagnostic tool has not yet been explored. This cross-sectional study aimed to provide a comprehensive, objective description of motor speech function in the parkinsonian (MSA-P) and cerebellar (MSA-C) variants of MSA. Speech samples were acquired from 80 participants including 18 MSA-P, 22 MSA-C, 20 Parkinson’s disease (PD), and 20 healthy controls. The accurate differential diagnosis of dysarthria subtypes was based on quantitative acoustic analysis of 14 speech dimensions. A mixed type of dysarthria involving hypokinetic, ataxic and spastic components was found in the majority of MSA patients independent of phenotype. MSA-P showed significantly greater speech impairment than PD, and predominantly exhibited harsh voice, imprecise consonants, articulatory decay, monopitch, excess pitch fluctuation and pitch breaks. MSA-C was dominated by prolonged phonemes, audible inspirations and voice stoppages. Inappropriate silences, irregular motion rates and overall slowness of speech were present in both MSA phenotypes. Speech features allowed discrimination between MSA-P and PD as well as between both MSA phenotypes with an area under curve up to 0.86. Hypokinetic, ataxic and spastic dysarthria components in MSA were correlated to the clinical evaluation of rigidity, cerebellar and bulbar/pseudobulbar manifestations, respectively. Distinctive speech alterations reflect underlying pathophysiology in MSA. Objective speech assessment may provide an inexpensive and widely applicable screening instrument for differentiation of MSA and PD from controls and among subtypes of MSA.

Parkinson’s disease (PD) and essential tremor (ET) are prevalent movement disorders that mainly affect elderly people, presenting diagnostic challenges due to shared clinical features. While both disorders exhibit distinct speech patterns—hypokinetic dysarthria in PD and hyperkinetic dysarthria in ET—the efficacy of speech assessment for differentiation remains unexplored. Developing technology for automatic discrimination could enable early diagnosis and continuous monitoring. However, the lack of data for investigating speech behavior in these patients has inhibited the development of a framework for diagnostic support. In addition, phonetic variability across languages poses practical challenges in establishing a universal speech assessment system. Therefore, it is necessary to develop models robust to the phonetic variability present in different languages worldwide. We propose a method based on Gaussian mixture models to assess domain adaptation from models trained in German and Spanish to classify PD and ET patients in Czech. We modeled three different speech dimensions: articulation, phonation, and prosody and evaluated the models’ performance in both bi-class and tri-class classification scenarios (with the addition of healthy controls). Our results show that a fusion of the three speech dimensions achieved optimal results in binary classification, with accuracies up to 81.4 and 86.2% for monologue and /pa-ta-ka/ tasks, respectively. In tri-class scenarios, incorporating healthy speech signals resulted in accuracies of 63.3 and 71.6% for monologue and /pa-ta-ka/ tasks, respectively. Our findings suggest that automated speech analysis, combined with machine learning is robust, accurate, and can be adapted to different languages to distinguish between PD and ET patients.

The effect of dopaminergic medication on speech has rarely been examined in early-stage Parkinson’s disease (PD) and the respective literature is inconclusive and limited by inappropriate design with lack of PD control group. The study aims to examine the short-term effect of dopaminergic medication on speech in PD using patients with good motor responsiveness to levodopa challenge compared to a control group of PD patients with poor motor responsiveness. A total of 60 early-stage PD patients were investigated before (OFF) and after (ON) acute levodopa challenge and compared to 30 age-matched healthy controls. PD patients were categorised into two clinical subgroups (PD responders vs. PD nonresponders) according to the comparison of their motor performance based on movement disorder society-unified Parkinson’s disease rating scale, part III. Seven distinctive parameters of hypokinetic dysarthria were examined using quantitative acoustic analysis. We observed increased monopitch ( p  > 0.01), aggravated monoloudness ( p  > 0.05) and longer duration of stop consonants ( p  > 0.05) in PD compared to healthy controls, confirming the presence of hypokinetic dysarthria in early PD. No speech alterations from OFF to ON state were revealed in any of the two PD groups and speech dimensions investigated including monopitch, monoloudness, imprecise consonants, harsh voice, slow sequential motion rates, articulation rate, or inappropriate silences, although a subgroup of PD responders manifested obvious improvement in motor function after levodopa intake ( p  > 0.001). Since the short-term usage of levodopa does not easily affect voice and speech performance in PD, speech assessment may provide a medication state-independent motor biomarker of PD.

Substantial variability and severity of dysarthric patterns across Parkinson’s disease (PD) patients may reflect distinct phenotypic differences. We aimed to compare patterns of speech disorder in early-onset PD (EOPD) and late-onset PD (LOPD) in drug-naive patients at early stages of disease. Speech samples were acquired from a total of 96 participants, including two subgroups of 24 de-novo PD patients and two subgroups of 24 age- and sex-matched young and old healthy controls. The EOPD group included patients with age at onset below 51 (mean 42.6, standard deviation 6.1) years and LOPD group patients with age at onset above 69 (mean 73.9, standard deviation 3.0) years. Quantitative acoustic vocal assessment of 10 unique speech dimensions related to respiration, phonation, articulation, prosody, and speech timing was performed. Despite similar perceptual dysarthria severity in both PD subgroups, EOPD showed weaker inspirations (p = 0.03), while LOPD was characterized by decreased voice quality (p = 0.02) and imprecise consonant articulation (p = 0.03). In addition, age-independent occurrence of monopitch (p < 0.001), monoloudness (p = 0.008), and articulatory decay (p = 0.04) was observed in both PD subgroups. The worsening of consonant articulation was correlated with the severity of axial gait symptoms (r = 0.38, p = 0.008). Speech abnormalities in EOPD and LOPD share common features but also show phenotype-specific characteristics, likely reflecting the influence of aging on the process of neurodegeneration. The distinct pattern of imprecise consonant articulation can be interpreted as an axial motor symptom of PD.

Even though hypomimia is a hallmark of Parkinson’s disease (PD), objective and easily interpretable tools to capture the disruption of spontaneous and deliberate facial movements are lacking. This study aimed to develop a fully automatic video-based hypomimia assessment tool and estimate the prevalence and characteristics of hypomimia in de-novo PD patients with relation to clinical and dopamine transporter imaging markers. For this cross-sectional study, video samples of spontaneous speech were collected from 91 de-novo, drug-naïve PD participants and 75 age and sex-matched healthy controls. Twelve facial markers covering areas of forehead, nose root, eyebrows, eyes, lateral canthal areas, cheeks, mouth, and jaw were used to quantitatively describe facial dynamics. All patients were evaluated using Movement Disorder Society-Unified PD Rating Scale and Dopamine Transporter Single-Photon Emission Computed Tomography. Newly developed automated facial analysis tool enabled high-accuracy discrimination between PD and controls with area under the curve of 0.87. The prevalence of hypomimia in de-novo PD cohort was 57%, mainly associated with dysfunction of mouth and jaw movements, and decreased variability in forehead and nose root wrinkles ( p  < 0.001). Strongest correlation was found between reduction of lower lip movements and nigro-putaminal dopaminergic loss ( r  = 0.32, p  = 0.002) as well as limb bradykinesia/rigidity scores ( r  = −0.37 p  < 0.001). Hypomimia represents a frequent, early marker of motor impairment in PD that can be robustly assessed via automatic video-based analysis. Our results support an association between striatal dopaminergic deficit and hypomimia in PD.

Although dysphonia has been shown to be a common sign of Huntington disease (HD), the extent of phonatory dysfunction in gene positive premanifest HD individuals remains unknown. The aim of the current study was to explore the possible occurrence of phonatory abnormalities in prodromal HD. Sustained vowel phonations were acquired from 28 premanifest HD individuals and 28 healthy controls of comparable age. Data were analysed acoustically for measures of several phonatory dimensions including airflow insufficiency, aperiodicity, irregular vibration of vocal folds, signal perturbations, increased noise, vocal tremor and articulation deficiency. A predictive model was built to find the best combination of acoustic features and estimate sensitivity/specificity for differentiation between premanifest HD subjects and controls. The extent of voice deficits according to a specific phonatory dimension was determined using statistical decision making theory. The results were correlated to global motor function, cognitive score, disease burden score and estimated years to disease onset. Measures of aperiodicity and increased noise were able to significantly differentiate between premanifest HD individuals and controls (p<0.01). The combination of these aspects of dysphonia led to a sensitivity of 91.5% and specificity of 79.2% to correctly distinguish speakers with premanifest HD from healthy individuals. Some form of disrupted phonatory function was revealed in 68% of our premanifest HD subjects, where 18% had one affected phonatory dimension and 50% showed impairment of two or more dimensions. A relationship between pitch control and cognitive score was also observed (r =  -0.50, p =  0.007). Phonatory abnormalities are detectable even the in premotor stages of HD. Speech investigation may have the potential to provide functional biomarkers of HD and could be included in future clinical trials and therapeutic interventions.

When magnetic properties are analysed in a transmission electron microscope using the technique of electron magnetic circular dichroism (EMCD), one of the critical parameters is the sample orientation. Since small orientation changes can have a strong impact on the measurement of the EMCD signal and such measurements need two separate measurements of conjugate EELS spectra, it is experimentally non-trivial to measure the EMCD signal as a function of sample orientation. Here, we have developed a methodology to simultaneously map the quantitative EMCD signals and the local orientation of the crystal. We analyse, both experimentally and by simulations, how the measured magnetic signals evolve with a change in the crystal tilt. Based on this analysis, we establish an accurate relationship between the crystal orientations and the EMCD signals. Our results demonstrate that a small variation in crystal tilt can significantly alter the strength of the EMCD signal. From an optimisation of the crystal orientation, we obtain quantitative EMCD measurements.