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Remote and passive speech monitoring application
Klimeš, Jiří ; Mikulec, Marek (referee) ; Kováč, Daniel (advisor)
Motor speech disorders in patients with Parkinson’s disease collectively referred to as hypokinetic dysarthria, occur in the early phase of the disease. Language plays an essential role in classifying speakers into healthy and those with dysarthria. Author explains which aspects of speech are most often affected. Then explains how mobile applications work on the Android operating system, and if it is possible to use them in passive and distant speech monitoring. Then the topic of voice call recording is described and how is it possible to implement this solution. Such application is then designed and partially developed.
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Analysis of speech disorders in patients with a high risk of developing Lewy body diseases
Novotný, Kryštof ; Kováč, Daniel (referee) ; Mekyska, Jiří (advisor)
Lewy bodies diseases (one of the most common neurodegenerative disorders) have the same pathological basis, but the individual representatives differ in their clinical manifestations. Different diseases affect the mental or physical side of the patient to a greater or lesser extent. This work assumes that thanks to the acoustic analysis of speech, it is possible to distinguish individual diseases from one another, because the disorders of the cognitive and motor aspects of a patient reflect in speech in specific ways. The thesis aims to describe the clinical features of the main representatives of the Lewy bodies diseases, to investigate their impact on speech, to propose characterizing acoustic parameters and then to compare their discriminative power. Speech recordings from the CoBeN and preLBD databases are used as input data for the proposed algorithm. Descriptive statistics, Mann-Whitney U test, FDR correction and XGBoost machine learning model using stratified cross-validation and balanced accuracy are used for subsequent evaluation. The result are scripts for the automated calculation of speech parameters from the database and their evaluation. The results of the analysis prove that the selected diseases can really be distinguished from each other and from a healthy control based on the manifestations in speech, already in the prodromal stages.
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Development of features quantifying respiratory dysfunctions in Parkinson’s disease patients
Cvetler, Dominik ; Mekyska, Jiří (referee) ; Kováč, Daniel (advisor)
In the beginning of the thesis, Parkinson's disease and hypokinetic dysarthria are briefly described, which have a negative effect on speech production and cause breathing problems during speech in sick patients. The aim of the thesis is to create an algorithm for automated detection of breaths and the design of parameters for the quantification of respiratory disorders in patients with Parkinson's disease. In the MATLAB environment, the recordings of the researched subjects were processed and an algorithm was created for the detection of breaths, which used the logistic regression method. Based on the predicted breaths, proposed parameters were extracted from the recordings, which were then statistically analyzed and compared in healthy controls and patients with Parkinson's disease. By using a machine learning model, it was possible to predict the clinical data of patients from the proposed parameters to a certain extent. The average accuracy of the model for predicting puffs was 0.85. Of the 14 proposed parameters, 6 were suitable for quantifying respiratory disorders associated with hypokinetic dysarthria. The result of the work is a functional algorithm for the automated detection of breaths in the speech signal and proposed parameters that could be useful for the quantification of respiratory disorders in patients with Parkinson's disease.
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Sub-types of hypokinetic dysarthria in patients with moderete Parkinson's disease
Adamják, Adam ; Kováč, Daniel (referee) ; Mekyska, Jiří (advisor)
This final thesis deals with the research of Parkinson's disease, hypokinetic dysarthria, and acoustic and statistical analyses. Hypokinetic dysarthria is a speech disorder that is a typical manifestation of Parkinson's disease, a neurodegenerative disease that affects approximately 2% of the population over the age of 65. The aim of this work is to reveal the subtypes of hypokinetic dysarthria, based on clinical parameters, acoustic analysis, and statistical analysis. In the acoustic analysis, parameters that examine the area of phonation, prosody, articulation, and speech tempo have been implemented. Subsequently, a statistical analysis was processed, thanks to which it was possible to reveal the subtypes of hypokinetic dysarthria.
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Assessing Movement of Articulatory Organs in Patients with Parkinson’s Disease
Novotný, K. ; Mekyska, J.
Hypokinetic dysarthria is a motor speech disorder often present during Parkinson’s disease. It affects the speech system, including articulatory abilities. There are several speech parameters describing this domain, so it is suggested to deal with their mutual comparison. This work aims to design and describe an algorithm for calculating the parameters of articulation, adapted for the Czech language, and then compare their discriminative power. The acoustic analysis of speech included in it is done via the Praat program and basic machine learning algorithms such as Expectation-Maximization, K-means and linear regression are used for the subsequent data processing. The Mann-Whitney U test, descriptive statistics and Random Forest machine learning model using cross-validation and balanced accuracy is used for evaluation. The results are scripts for automatic assessment of vowel space area, for calculating articulation parameters and for their evaluation. The outputs of the analysis of speech recording database prove that differences in articulation can indeed be observed between normal and dysarthric speech. Based on the mutual comparison of results, it is therefore proposed in the work which parameters are being appropriate for further dealing with this issue.
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Analysis of impact of noise in recordings on the automated detection of hypokinetic dysarthria
Havelková, Nikola ; Galáž, Zoltán (referee) ; Kováč, Daniel (advisor)
This thesis deals with the automated detection of hypokinetic dysarthria by analysing the influence of noise present in recordings. Appropriate single-channel methods, specifically the spectral subtraction and Kalman filter, are selected and implemented in the MATLAB R2022a to enhance speech. These methods are also used for noise-free recordings, to which additive white noise was added. Afterwards, the effectiveness of these methods is objectively evaluated by using signal-to-noise ratio values. After enhancing of speech, interferences are extracted from the recordings. The effect of the presence of noise, as well as its subsequent suppression by individual methods, is then evaluated by statistical analysis, specifically using the Kruskal-Wallis test and the post hoc Dunn’s test. The probability of distributing parameters of clean, noisy and enhanced recordings, for which the effect of noise is significant, according to statistical tests, are plotted using violin and box graphs. Finally, the classification was done by logistic regression with the help of machine learning, where the effect of the presence of noise and subsequent speech enhancement on automated detection of hypokinetic dysarthria was described according to the area values under the ROC curve.
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Multilingual Analysis Of Hypokinetic Dysarthria In Patients With Parkinson’s Disease
Kováč, Daniel
This article deals with the multilingual analysis of hypokinetic dysarthria (HD) in patientswith Parkinson’s disease (PD). The goal is to identify acoustic features that have high discriminationpower and that are independent of the language of a speaker. The speech corpus contains 59 PD patientsand 44 healthy controls (HC) speaking in Czech (cs) and American English (en-US). Based onnon-parametric statistical tests and logistic regression, we observed the best discrimination power hasthe speech index of rhythmicity (extracted from a reading text) and harmonic-to-noise ratio (extractedfrom a sustained vowel). We were able to identify PD with 67% sensitivity and 79% specificity inthe Czech corpus and with 78% sensitivity and 67% specificity in the English one. The performanceof the model was significantly lower when combining both datasets, thus suggesting language playsa significant role during the automatic assessment of HD.
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Assessing movement of articulatory organs based on acoustic analysis of speech
Novotný, Kryštof ; Galáž, Zoltán (referee) ; Mekyska, Jiří (advisor)
Hypokinetic dysarthria is a motor speech disorder often present during Parkinson’s disease. It affects the speech system, including articulatory abilities. There are several speech parameters describing this domain, so it is suggested to deal with their mutual comparison. This work aims to design and describe an algorithm for calculating the parameters of articulation, adapted for the Czech language, and then compare their discriminative power. The acoustic analysis of speech included in it is done via the Praat program and basic machine learning algorithms such as Expectation-Maximization, Kmeans and linear regression are used for the subsequent data processing. The Mann-Whitney U test and representatives of linear, nonlinear and ensemble machine learning models using cross-validation and balanced accuracy are used for evaluation. The results are scripts for automatic assessment of vowel space area, for calculating articulation parameters and for their evaluation. The outputs of the analysis of two different databases (PARCZ and CoBeN) prove that differences in articulation can indeed be observed between normal and dysarthric speech. Based on the mutual comparison of results, it is therefore proposed in the work which parameters and models of machine learning are being appropriate for further dealing with this issue.
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Identification Of Parkinson’S Disease Using Acousticanalysis Of Poem Recitation
Mucha, Ján
Parkinson’s disease (PD) is the second most frequent neurodegenerative disorder. It is estimated that 60–90% of PD patients suffer from speech disorder called hypokinetic dysarthria (HD). The goal of this work is to reveal influence of poem recitation on acoustic analysis of speech and propose concept of Parkinson’s disease identification based on this analysis. Classification methods used in this work are Random Forests and Support Vector Machine. The best achieved accuracy of disease identification is 70.66% with 59.25% sensitivity for Random Forests classifier fed mainly with articulation features. These results demonstrate a high potential of research in this area.
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Speech Disorders In Parkinson’S Disease Patients With Mild Form Of Freezing Of Gait
Galáž, Zoltán
This paper deals with the description of speech disorders present in the mild stage of freezing of gait (FOG) in patients with Parkinson’s disease (PD). Experimental dataset consisted of 48 PD patients and 52 healthy controls (HC). We used freezing of gait questionnaire (FOG-Q) to characterize FOG in PD. Using one-way analysis of variance, we found loosely adducted vocal folds during phonation (p = 0.0027), increased acoustic noise (p = 0.0294), reduced variability of pitch (p = 0.0440), and reduced mobility of articulatory organs (p = 0.0157) significantly statistically different in PD patients in comparison with HC.
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