Ing. Vojtěch Illner, Ph.D.

Over the past decade, neuropsychiatric fluctuations in Parkinson's disease (PD) have been increasingly recognized for their impact on patients' quality of life. Speech, a complex function carrying motor, emotional, and cognitive information, offers potential insights into these fluctuations. While previous studies have focused on acoustic analysis to assess motor speech disorders reliably, the potential of linguistic patterns associated with neuropsychiatric fluctuations in PD remains unexplored. This study analyzed the content of spontaneous speech from 33 PD patients in ON and OFF medication states, using machine learning and large language models (LLMs) to predict medication states and a neuropsychiatric state score. The top-performing model, the LLM Gemma-2 (9B), achieved 98% accuracy in differentiating ON and OFF states and its predicted scores were highly correlated with actual scores (Spearman's rho = 0.81). These methods could provide a more comprehensive assessment of PD treatment effects, allowing remote neuropsychiatric symptom monitoring via mobile devices.

Background Research on the possible influence of lateralised basal ganglia dysfunction on speech in Parkinson's disease is scarce. This study aimed to compare speech in de-novo, drug-naive patients with Parkinson's disease (PD) with asymmetric nigral dopaminergic dysfunction, predominantly in either the right or left hemisphere. Methods Acoustic analyses of reading passages were performed. Asymmetry of nigral dysfunction was defined using dopamine transporter-single-photon emission CT (DAT-SPECT). Results From a total of 135 de novo patients with PD assessed, 47 patients had a lower right and 36 lower left DAT availability in putamen based on DAT-SPECT. Patients with PD with lower left DAT availability had higher dysarthria severity via composite dysarthria index compared with patients with lower right DAT availability (p=0.01). Conclusion Our data support the crucial role of DAT availability in the left putamen in speech. This finding might provide important clues for managing speech following deep brain stimulation.

Purpose: Although articulatory impairment represents distinct speech characteristics in most neurological diseases affecting movement, methods allowing automated assessments of articulation deficits from the connected speech are scarce. This study aimed to design a fully automated method for analyzing dysarthriarelated vowel articulation impairment and estimate its sensitivity in a broad range of neurological diseases and various types and severities of dysarthria.Method: Unconstrained monologue and reading passages were acquired from 459 speakers, including 306 healthy controls and 153 neurological patients. The algorithm utilized a formant tracker in combination with a phoneme recognizer and subsequent signal processing analysis.Results: Articulatory undershoot of vowels was presented in a broad spectrum of progressive neurodegenerative diseases, including Parkinson's disease, progressive supranuclear palsy, multiple-system atrophy, Huntington's disease, essential tremor, cerebellar ataxia, multiple sclerosis, and amyotrophic lateral sclerosis, as well as in related dysarthria subtypes including hypokinetic, hyper kinetic, ataxic, spastic, flaccid, and their mixed variants. Formant ratios showed a higher sensitivity to vowel deficits than vowel space area. First formants of corner vowels were significantly lower for multiple-system atrophy than cerebellar ataxia. Second formants of vowels /a/ and /i/ were lower in ataxic compared to spastic dysarthria. Discriminant analysis showed a classification score of up to 41.0% for disease type, 39.3% for dysarthria type, and 49.2% for dysarthria severity. Algorithm accuracy reached an F-score of 0.77. Conclusions: Distinctive vowel articulation alterations reflect underlying pathophysiology in neurological diseases. Objective acoustic analysis of vowel articulation has the potential to provide a universal method to screen motor speech disorders.Supplemental Material: https://doi.org/10.23641/asha.23681529

Although long-term averaged spectrum (LTAS) descriptors can detect the change in dysarthria of patients with Parkinson's disease (PD) due to subthalamic nucleus deep brain stimulation (STN-DBS), the relationship between LTAS variables with measures that relate to laryngeal physiology remain unknown. We aimed to find connections between LTAS-based moments and the main acoustic characteristics of hypokinetic dysarthria in PD as the response to STN-DBS stimulation changes. We analyzed reading passages of 23 PD patients in ON and OFF STN-DBS states compared to 23 healthy controls. We found a relation between the stimulation-induced change in several spectral moments and acoustic parameters representing voice quality, articulatory decay, articulation rate, and mean fundamental frequency. While the difference between PD and controls was significant across most acoustic descriptors, only the spectral mean and fundamental frequency variability could differentiate between ON and OFF conditions.

One of the most popular speech parametrizations for dysarthria has been Mel Frequency Cepstral Coefficients (MFCCs). Although the MFCCs ability to capture vocal tract characteristics is known, the reflected dysarthria aspects are primarily undisclosed. Thus, we investigated the relationship between key acoustic variables in Parkinson's disease (PD) and the MFCCs. 23 PD patients were recruited with ON and OFF conditions of Deep Brain Stimulation of the Subthalamic Nucleus (STN-DBS) and examined via a reading passage. The changes in dysarthria aspects were compared to changes in a global MFCC measure and individual MFCCs. A similarity was found in 2nd to 3rd MFCCs changes and voice quality. Changes in 4th to 9th MFCCs reflected articulation clarity. The global MFCC parameter outperformed individual MFCCs and acoustical measures in capturing STN-DBS conditions changes. The findings may assist in interpreting outcomes from clinical trials and improve the monitoring of disease progression.

Introduction: Early 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 analysis: Speech 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 dissemination: The 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.

Purpose: This study aimed to evaluate the reliability of different approaches for estimating the articulation rates in connected speech of Parkinsonian patients with different stages of neurodegeneration compared to healthy controls. Method: Monologues and reading passages were obtained from 25 patients with idiopathic rapid eye movement sleep behavior disorder (iRBD), 25 de novo patients with Parkinson's disease (PD), 20 patients with multiple system atrophy (MSA), and 20 healthy controls. The recordings were subsequently evaluated using eight syllable localization algorithms, and their performances were compared to a manual transcript used as a reference. Results: The Google & Pyphen method, based on automatic speech recognition followed by hyphenation, outperformed the other approaches (automated vs. hand transcription: r > .87 for monologues and r > .91 for reading passages, p < .001) in precise feature estimates and resilience to dysarthric speech. The Praat script algorithm achieved sufficient robustness (automated vs. hand transcription: r > .65 for monologues and r > .78 for reading passages, p < .001). Compared to the control group, we detected a slow rate in patients with MSA and a tendency toward a slower rate in patients with iRBD, whereas the articulation rate was unchanged in patients with early untreated PD. Conclusions: The state-of-the-art speech recognition tool provided the most precise articulation rate estimates. If speech recognizer is not accessible, the freely available Praat script based on simple intensity thresholding might still provide robust properties even in severe dysarthria. Automated articulation rate assessment may serve as a natural, inexpensive biomarker for monitoring disease severity and a differential diagnosis of Parkinsonism

Measuring the fundamental frequency of the vocal folds F-0 is recognized as an important parameter in the assessment of speech impairments in Parkinson's disease (PD). Although a number of F-0 trackers currently exist, their performance in smartphone-based evaluation and robustness against background noise have never been tested. Monologues from 30 newly-diagnosed, untreated PD patients and 30 matched healthy control participants were collected. Additive non-stationary urban and household noise at different SNR levels was added to the recordings, which were subsequently assessed by 10 freely-available and widely-used pitch-tracking algorithms. According to the comparison of all investigated pitch detectors, sawtooth inspired pitch estimator (SWIPE) was the most robust and accurate method in estimating mean F-0 and its standard deviation. However, at a low 6 dB SNR level, a combination of more algorithms may be needed to achieve the desired precision. Monopitch, calculated as F-0 standard deviation and estimated by SWIPE, proved to be robust in distinguishing between the PD and healthy control groups (p < 0.001). We anticipate that monopitch may serve as a quick and inexpensive biomarker of disease progression based on longitudinal data collected via smartphone, without any logistical or time constraints for patients and physicians. (C) 2020 Elsevier Ltd. All rights reserved.