Zoltan Galaz

Prosodic analysis of neutral, stress-modified and rhymed speech in patients with Parkinson's disease

BACKGROUND AND OBJECTIVE Hypokinetic dysarthria (HD) is a frequent speech disorder associated with idiopathic Parkinsons disease (PD). It affects all dimensions of speech production. One of the most common features of HD is dysprosody that is characterized by alterations of rhythm and speech rate, flat speech melody, and impairment of speech intensity control. Dysprosody has a detrimental impact on speech naturalness and intelligibility. METHODS This paper deals with quantitative prosodic analysis of neutral, stress-modified and rhymed speech in patients with PD. The analysis of prosody is based on quantification of monopitch, monoloudness, and speech rate abnormalities. Experimental dataset consists of 98 patients with PD and 51 healthy speakers. For the purpose of HD identification, sequential floating feature selection algorithm and random forests classifier is used. In this paper, we also introduce a concept of permutation test applied in the field of acoustic analysis of dysarthric speech. RESULTS Prosodic features obtained from stress-modified reading task provided higher classification accuracies compared to the ones extracted from reading task with neutral emotion demonstrating the importance of stress in speech prosody. Features calculated from poem recitation task outperformed both reading tasks in the case of gender-undifferentiated analysis showing that rhythmical demands can in general lead to more precise identification of HD. Additionally, some gender-related patterns of dysprosody has been observed. CONCLUSIONS This paper confirms reduced variation of fundamental frequency in PD patients with HD. Interestingly, increased variability of speech intensity compared to healthy speakers has been detected. Regarding speech rate disturbances, our results does not report any particular pattern. We conclude further development of prosodic features quantifying the relationship between monopitch, monoloudness and speech rate disruptions in HD can have a great potential in future PD analysis.

Assessing progress of Parkinson's disease using acoustic analysis of phonation

This paper deals with a complex acoustic analysis of phonation in patients with Parkinsons disease (PD) with a special focus on estimation of disease progress that is described by 7 different clinical scales (e. g. Unified Parkinsons disease rating scale or Beck depression inventory). The analysis is based on parametrization of 5 Czech vowels pronounced by 84 PD patients. Using classification and regression trees we estimated all clinical scores with maximal error lower or equal to 13 %. Best estimation was observed in the case of Mini-mental state examination (MAE = 0.77, estimation error 5.50 %). Finally, we proposed a binary classification based on random forests that is able to identify Parkinsons disease with sensitivity SEN = 92.86% (SPE = 85.71 %). The parametrization process was based on extraction of 107 speech features quantifying different clinical signs of hypokinetic dysarthria present in PD.

Perceptual Features as Markers of Parkinson’s Disease: The Issue of Clinical Interpretability

Up to 90 % of patients with Parkinson’s disease (PD) suffer from hypokinetic dysathria (HD) which is also manifested in the field of phonation. Clinical signs of HD like monoloudness, monopitch or hoarse voice are usually quantified by conventional clinical interpretable features (jitter, shimmer, harmonic-to-noise ratio, etc.). This paper provides large and robust insight into perceptual analysis of 5 Czech vowels of 84 PD patients and proves that despite the clinical inexplicability the perceptual features outperform the conventional ones, especially in terms of discrimination power (classification accuracy \(\mathrm{ACC} = 92\) %, sensitivity \(\mathrm{SEN} = 93\) %, specificity \(\mathrm{SPE} = 92\) %) and partial correlation with clinical scores like UPDRS (Unified Parkinson’s disease rating scale), MMSE (Mini-mental state examination) or FOG (Freezing of gait questionnaire), where \(p < 0.0001\).

Identification and Rating of Developmental Dysgraphia by Handwriting Analysis

Developmental dysgraphia, being observed among 10–30% of school-aged children, is a disturbance or difficulty in the production of written language that has to do with the mechanics of writing. The objective of this study is to propose a method that can be used for automated diagnosis of this disorder, as well as for estimation of difficulty level as determined by the handwriting proficiency screening questionnaire. We used a digitizing tablet to acquire handwriting and consequently employed a complex parameterization in order to quantify its kinematic aspects and hidden complexities. We also introduced a simple intrawriter normalization that increased dysgraphia discrimination and HPSQ estimation accuracies. Using a random forest classifier, we reached 96% sensitivity and specificity, while in the case of automated rating by the HPSQ total score, we reached 10% estimation error. This study proves that digital parameterization of pressure and altitude/tilt patterns in children with dysgraphia can be used for preliminary diagnosis of this writing disorder.

Voice Pathology Detection Using Deep Learning: a Preliminary Study

This paper describes a preliminary investigation of Voice Pathology Detection using Deep Neural Networks (DNN). We used voice recordings of sustained vowel /a/ produced at normal pitch from German corpus Saarbruecken Voice Database (SVD). This corpus contains voice recordings and electroglottograph signals of more than 2 000 speakers. The idea behind this experiment is the use of convolutional layers in combination with recurrent Long-Short-Term-Memory (LSTM) layers on raw audio signal. Each recording was split into 64 ms Hamming windowed segments with 30 ms overlap. Our trained model achieved 71.36% accuracy with 65.04% sensitivity and 77.67% specificity on 206 validation files and 68.08% accuracy with 66.75% sensitivity and 77.89% specificity on 874 testing files. This is a promising result in favor of this approach because it is comparable to similar previously published experiment that used different methodology. Further investigation is needed to achieve the state-of-the-art results.

Parkinson Disease Detection from Speech Articulation Neuromechanics

Aim: The research described is intended to give a description of articulation dynamics as a correlate of the kinematic behavior of the jaw-tongue biomechanical system, encoded as a probability distribution of an absolute joint velocity. This distribution may be used in detecting and grading speech from patients affected by neurodegenerative illnesses, as Parkinson Disease. Hypothesis: The work hypothesis is that the probability density function of the absolute joint velocity includes information on the stability of phonation when applied to sustained vowels, as well as on fluency if applied to connected speech. Methods: A dataset of sustained vowels recorded from Parkinson Disease patients is contrasted with similar recordings from normative subjects. The probability distribution of the absolute kinematic velocity of the jaw-tongue system is extracted from each utterance. A Random Least Squares Feed-Forward Network (RLSFN) has been used as a binary classifier working on the pathological and normative datasets in a leave-one-out strategy. Monte Carlo simulations have been conducted to estimate the influence of the stochastic nature of the classifier. Two datasets for each gender were tested (males and females) including 26 normative and 53 pathological subjects in the male set, and 25 normative and 38 pathological in the female set. Results: Male and female data subsets were tested in single runs, yielding equal error rates under 0.6% (Accuracy over 99.4%). Due to the stochastic nature of each experiment, Monte Carlo runs were conducted to test the reliability of the methodology. The average detection results after 200 Montecarlo runs of a 200 hyperplane hidden layer RLSFN are given in terms of Sensitivity (males: 0.9946, females: 0.9942), Specificity (males: 0.9944, females: 0.9941) and Accuracy (males: 0.9945, females: 0.9942). The area under the ROC curve is 0.9947 (males) and 0.9945 (females). The equal error rate is 0.0054 (males) and 0.0057 (females). Conclusions: The proposed methodology avails that the use of highly normalized descriptors as the probability distribution of kinematic variables of vowel articulation stability, which has some interesting properties in terms of information theory, boosts the potential of simple yet powerful classifiers in producing quite acceptable detection results in Parkinson Disease.

Degree of Parkinson's disease severity estimation based on speech signal processing

This paper deals with Parkinsons disease (PD) severity estimation according to the Unified Parkinsons Disease Rating Scale: motor subscale (UPDRS III), which quantifies the hallmark symptoms of PD, using an acoustic analysis of speech signals. Experimental dataset comprised 42 speech tasks acquired from 50 PD patients (UPDRS in ranged from 6 to 92). It was divided into subsets: words, sentences, reading text, monologue and diadochokinetic tasks. We performed a parametrization of the whole corpus and these groups separately using a wide range of conventional and novel speech features. We used guided regularized random forest algorithm to select features with maximum clinical information and performed random forests regression to estimate PD severity. According to significant correlations between true UPDRS in scores and scores predicted by the proposed methodology it was shown that information extracted through variety of speech tasks can be used to estimate the degree of PD severity.

Non-invasive stimulation of the auditory feedback area for improved articulation in Parkinson's disease

INTRODUCTION Hypokinetic dysarthria (HD) is a common symptom of Parkinsons disease (PD) which does not respond well to PD treatments. We investigated acute effects of repetitive transcranial magnetic stimulation (rTMS) of the motor and auditory feedback area on HD in PD using acoustic analysis of speech. METHODS We used 10 Hz and 1 Hz stimulation protocols and applied rTMS over the left orofacial primary motor area, the right superior temporal gyrus (STG), and over the vertex (a control stimulation site) in 16 PD patients with HD. A cross-over design was used. Stimulation sites and protocols were randomised across subjects and sessions. Acoustic analysis of a sentence reading task performed inside the MR scanner was used to evaluate rTMS-induced effects on motor speech. Acute fMRI changes due to rTMS were also analysed. RESULTS The 1 Hz STG stimulation produced significant increases of the relative standard deviation of the 2nd formant (p = 0.019), i.e. an acoustic parameter describing the tongue and jaw movements. The effects were superior to the control site stimulation and were accompanied by increased resting state functional connectivity between the stimulated region and the right parahippocampal gyrus. The rTMS-induced acoustic changes were correlated with the reading task-related BOLD signal increases of the stimulated area (R = 0.654, p = 0.029). CONCLUSION Our results demonstrate for the first time that low-frequency stimulation of the temporal auditory feedback area may improve articulation in PD and enhance functional connectivity between the STG and the cortical region involved in an overt speech control.

Quantitative Analysis of Relationship Between Hypokinetic Dysarthria and the Freezing of Gait in Parkinson’s Disease

Hypokinetic dysarthria (HD) and freezing of gait (FOG) are both axial symptoms that occur in patients with Parkinson’s disease (PD). It is assumed they have some common pathophysiological mechanisms and therefore that speech disorders in PD can predict FOG deficits within the horizon of some years. The aim of this study is to employ a complex quantitative analysis of the phonation, articulation and prosody in PD patients in order to identify the relationship between HD and FOG, and establish a mathematical model that would predict FOG deficits using acoustic analysis at baseline. We enrolled 75 PD patients who were assessed by 6 clinical scales including the Freezing of Gait Questionnaire (FOG–Q). We subsequently extracted 19 acoustic measures quantifying speech disorders in the fields of phonation, articulation and prosody. To identify the relationship between HD and FOG, we performed a partial correlation analysis. Finally, based on the selected acoustic measures, we trained regression models to predict the change in FOG during a 2-year follow-up. We identified significant correlations between FOG–Q scores and the acoustic measures based on formant frequencies (quantifying the movement of the tongue and jaw) and speech rate. Using the regression models, we were able to predict a change in particular FOG–Q scores with an error of between 7.4 and 17.0 %. This study is suggesting that FOG in patients with PD is mainly linked to improper articulation, a disturbed speech rate and to intelligibility. We have also proved that the acoustic analysis of HD at the baseline can be used as a predictor of the FOG deficit during 2 years of follow-up. This knowledge enables researchers to introduce new cognitive systems that predict gait difficulties in PD patients.

Identification of hypokinetic dysarthria using acoustic analysis of poem recitation

Up to 90% of patients with Parkinsons disease (PD) suffer from hypokinetic dysarthria (HD). In this work, we analysed the power of conventional speech features quantifying imprecise articulation, dysprosody, speech dysfluency and speech quality deterioration extracted from a specialized poem recitation task to discriminate dysarthric and healthy speech. For this purpose, 152 speakers (53 healthy speakers, 99 PD patients) were examined. Only mildly strong correlation between speech features and clinical status of the speakers was observed. In case of univariate classification analysis, sensitivity of 62.63 % (imprecise articulation), 61.62% (dysprosody), 71.72% (speech dysfluency) and 59.60% (speech quality deterioration) was achieved. Multivariate classification analysis improved the classification performance. Sensitivity of 83.42% using only two features describing imprecise articulation and speech quality deterioration in HD was achieved. We showed the promising potential of the selected speech features and especially the use of poem recitation task to quantify and identify HD in PD.