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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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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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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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Differential analysis of multilingual corpus in patients with neurodegenerative diseases
Kováč, Daniel ; Zvončák, Vojtěch (referee) ; Mekyska, Jiří (advisor)
This diploma thesis focuses on the automated diagnosis of hypokinetic dysarthria in the multilingual speech corpus, which is a motor speech disorder that occurs in patients with neurodegenerative diseases such as Parkinson’s disease. The automatic speech recognition approach to diagnosis is based on the acoustic analysis of speech and subsequent use of mathematical models. The popularity of this method is on the rise due to its objectivity and the possibility of working simultaneously on different languages. The aim of this work is to find out which acoustic parameters have high discriminative power and are universal for multiple languages. To achieve this, a statistical analysis of parameterized speech tasks and subsequent modelling by machine learning methods was used. The analyses were performed for Czech, American English, Hungarian and all languages together. It was found that only some parameters enable the diagnosis of the hypokinetic disorder and are, at the same time, universal for multiple languages. The relF2SD parameter shows the best results, followed by the NST parameter. When classifying speakers of all the languages together, the model achieves accuracy of 59 % and sensitivity of 72 %.
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Development of modern acoustic features quantifying hypokinetic dysarthria
Kowolowski, Alexander ; Zvončák, Vojtěch (referee) ; Galáž, Zoltán (advisor)
This work deals with designing and testing of new acoustic features for analysis of dysprosodic speech occurring in hypokinetic dysarthria patients. 41 new features for dysprosody quantification (describing melody, loudness, rhythm and pace) are presented and tested in this work. New features can be divided into 7 groups. Inside the groups, features vary by the used statistical values. First four groups are based on absolute differences and cumulative sums of fundamental frequency and short-time energy of the signal. Fifth group contains features based on multiples of this fundamental frequency and short-time energy combined into one global intonation feature. Sixth group contains global time features, which are made of divisions between conventional rhythm and pace features. Last group contains global features for quantification of whole dysprosody, made of divisions between global intonation and global time features. All features were tested on Czech Parkinsonian speech database PARCZ. First, kernel density estimation was made and plotted for all features. Then correlation analysis with medicinal metadata was made, first for all the features, then for global features only. Next classification and regression analysis were made, using classification and regression trees algorithm (CART). This analysis was first made for all the features separately, then for all the data at once and eventually a sequential floating feature selection was made, to find out the best fitting combination of features for the current matter. Even though none of the features emerged as a universal best, there were a few features, that were appearing as one of the best repeatedly and also there was a trend that there was a bigger drop between the best and the second best feature, marking it as a much better feature for the given matter, than the rest of the tested. Results are included in the conclusion together with the discussion.
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Research of modern articulation features for the analysis of hypokinetic dysarthria
Vrba, Filip ; Zvončák, Vojtěch (referee) ; Galáž, Zoltán (advisor)
This thesis deals with hypokinetic dysarthria, as a disorder of motor speech, which occurs in approximately 70% of patients with Parkinson’s disease (PD). Two newly designed speech parameters for quantification of articulation within HD are analysed in this thesis. This parameters were validated on recording of both healthy and PD speakers. The theoretical part describes conventional and used methods of speech signal processing, parameterization and statistical analysis. In the part of the system implementation is described practical design of new parameters and also methods of their statistical evaluation by correlation analysis and machine learning. The aim of this work is to design new speech parameters for HD diagnostics. The proposed system was implemented in MATLAB software environment.
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Assessment of Parkinson’s Disease Based on Acoustic Analysis of Hypokinetic Dysarthria
Galáž, Zoltán ; Brezany, Peter (referee) ; Sklenář, Jaroslav (referee) ; Mekyska, Jiří (advisor)
Hypokinetická dysartrie (HD) je častým symptomem vyskytujícím se až u 90% pacientů trpících idiopatickou Parkinsonovou nemocí (PN), která výrazně přispívá k nepřirozenosti a nesrozumitelnosti řeči těchto pacientů. Hlavním cílem této disertační práce je prozkoumat možnosti použití kvantitativní paraklinické analýzy HD, s použitím parametrizace řeči, statistického zpracování a strojového učení, za účelem diagnózy a objektivního hodnocení PN. Tato práce dokazuje, že počítačová akustická analýza je schopná dostatečně popsat HD, speciálně tzv. dysprozodii, která se projevuje nedokonalou intonací a nepřirozeným tempem řeči. Navíc také dokazuje, že použití klinicky interpretovatelných akustických parametrů kvantifikujících různé aspekty HD, jako jsou fonace, artikulace a prozodie, může být použito k objektivnímu posouzení závažnosti motorických a nemotorických symptomů vyskytujících se u pacientů s PN. Dále tato práce prezentuje výzkum společných patofyziologických mechanizmů stojících za HD a zárazy v chůzi při PN. Nakonec tato práce dokazuje, že akustická analýza HD může být použita pro odhad progrese zárazů v chůzi v horizontu dvou let.
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Speech therapy for Parkinson's disease
Procházková, Eva ; Klenková, Jiřina (advisor) ; Kopečný, Petr (referee)
This diploma thesis presents an analysis of speech impairments accompanying Parkinson's disease. This paper is divided into two sections - theoretical and practical. The first section gives a brief overview of available Czech and foreign literature and articles about this neurodegenerative disease. The section examines the questions of its aetiology, symptomatology, diagnostics and treatment. There can be found also a description of speech impairment connected with this disease, which is mainly hypokinetic and hyperkinetic dysarthria and dysphagia. It also deals with the problem of other limitations in communication such as facial bradykinesia or speech intelligibility. In the last chapter of theoretical part is described speech therapy and intervention with the emphasis on therapy, diagnostics and the effects of pharmacology an non-pharmacological treatment such as deep brain stimulation on speech performance in Parkinson's disease. The research part analyses speech impairment of people with Parkinson's disease. The aim of this diploma thesis was examination of this speech impairment using the Test 3F: Dysarthria profile and patient's own perception of this specific speech disorder. In this paper are presented eight case studies focusing on speech of clients with Parkinson's disease. The results...
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