Embodied cognition research on Parkinson's disease (PD) points to disruptions of frontostriatal language functions as sensitive targets for clinical assessment. However, no existing approach has been tested for crosslinguistic validity, let alone by combining naturalistic tasks with machine-learning tools. To address these issues, we conducted the first classifier-based examination of morphological processing (a core frontostriatal function) in spontaneous monologues from PD patients across three typologically different languages. The study comprised 330 participants, encompassing speakers of Spanish (61 patients, 57 matched controls), German (88 patients, 88 matched controls), and Czech (20 patients, 16 matched controls). All subjects described the activities they perform during a regular day, and their monologues were automatically coded via morphological tagging, a computerized method that labels each word with a part-of-speech tag (e.g., noun, verb) and specific morphological tags (e.g., person, gender, number, tense). The ensuing data were subjected to machine-learning analyses to assess whether differential morphological patterns could classify between patients and controls and reflect the former's degree of motor impairment. Results showed robust classification rates, with over 80% of patients being discriminated from controls in each language separately. Moreover, the most discriminative morphological features were associated with the patients' motor compromise (as indicated by Pearson r correlations between predicted and collected motor impairment scores that ranged from moderate to moderate-to-strong across languages). Taken together, our results suggest that morphological patterning, an embodied frontostriatal domain, may be distinctively affected in PD across languages and even under ecological testing conditions.

• MeSH
• jazyk (prostředek komunikace) * MeSH
• kognice MeSH
• lidé MeSH
• Parkinsonova nemoc * MeSH
• řeč MeSH
• strojové učení MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

Automatic voice assessment is often performed using sustained vowels. In contrast, speech analysis of read-out texts can be applied to voice and speech assessment. Automatic speech recognition and prosodic analysis were used to find regression formulae between automatic and perceptual assessment of four voice and four speech criteria. The regression was trained with 21 men and 62 women (average age 49.2 years) and tested with another set of 24 men and 49 women (48.3 years), all suffering from chronic hoarseness. They read the text 'Der Nordwind und die Sonne' ('The North Wind and the Sun'). Five voice and speech therapists evaluated the data on 5-point Likert scales. Ten prosodic and recognition accuracy measures (features) were identified which describe all the examined criteria. Inter-rater correlation within the expert group was between r = 0.63 for the criterion 'match of breath and sense units' and r = 0.87 for the overall voice quality. Human-machine correlation was between r = 0.40 for the match of breath and sense units and r = 0.82 for intelligibility. The perceptual ratings of different criteria were highly correlated with each other. Likewise, the feature sets modeling the criteria were very similar. The automatic method is suitable for assessing chronic hoarseness in general and for subgroups of functional and organic dysphonia. In its current version, it is almost as reliable as a randomly picked rater from a group of voice and speech therapists.

• MeSH
• akustika řeči * MeSH
• akustika MeSH
• chrapot diagnóza   patofyziologie   MeSH
• chronická nemoc MeSH
• čtení MeSH
• dospělí MeSH
• kvalita hlasu * MeSH
• lidé středního věku MeSH
• lidé MeSH
• měření tvorby řeči metody   MeSH
• mladiství MeSH
• mladý dospělý MeSH
• počítačové zpracování signálu * MeSH
• prediktivní hodnota testů MeSH
• regresní analýza MeSH
• reprodukovatelnost výsledků MeSH
• rozpoznávání automatizované * MeSH
• senioři nad 80 let MeSH
• senioři MeSH
• support vector machine MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mladiství MeSH
• mladý dospělý MeSH
• mužské pohlaví MeSH
• senioři nad 80 let MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH

Due to low intra- and interrater reliability, perceptual voice evaluation should be supported by objective, automatic methods. In this study, text-based, computer-aided prosodic analysis and measurements of connected speech were combined in order to model perceptual evaluation of the German Roughness-Breathiness-Hoarseness (RBH) scheme. 58 connected speech samples (43 women and 15 men; 48.7 ± 17.8 years) containing the German version of the text "The North Wind and the Sun" were evaluated perceptually by 19 speech and voice therapy students according to the RBH scale. For the human-machine correlation, Support Vector Regression with measurements of the vocal fold cycle irregularities (CFx) and the closed phases of vocal fold vibration (CQx) of the Laryngograph and 33 features from a prosodic analysis module were used to model the listeners' ratings. The best human-machine results for roughness were obtained from a combination of six prosodic features and CFx (r = 0.71, ρ = 0.57). These correlations were approximately the same as the interrater agreement among human raters (r = 0.65, ρ = 0.61). CQx was one of the substantial features of the hoarseness model. For hoarseness and breathiness, the human-machine agreement was substantially lower. Nevertheless, the automatic analysis method can serve as the basis for a meaningful objective support for perceptual analysis.

• MeSH
• chrapot diagnóza   MeSH
• dítě MeSH
• dospělí MeSH
• kvalita hlasu * MeSH
• lidé středního věku MeSH
• lidé MeSH
• mladiství MeSH
• mladý dospělý MeSH
• percepce řeči MeSH
• počítačové zpracování signálu * MeSH
• poruchy hlasu diagnóza   MeSH
• řeč * MeSH
• řečová terapie MeSH
• regresní analýza MeSH
• reprodukovatelnost výsledků MeSH
• senioři nad 80 let MeSH
• senioři MeSH
• software MeSH
• zvuková spektrografie metody   MeSH
• Check Tag
• dítě MeSH
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mladiství MeSH
• mladý dospělý MeSH
• mužské pohlaví MeSH
• senioři nad 80 let MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH