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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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Simulation of Noise Emitted by a Single-stage Gearbox
Motl, Daniel ; Hadaš, Zdeněk (referee) ; Lošák, Petr (advisor)
The level of noise and vibration of gearboxes is one of the most important parameters for today´s customers. This thesis deals with mathematical acoustic behavior modelling and its contribution to the gearbox design using finite element method. The process is presented at three computations models. Two of those were validated with experimental measurement. Acoustic analysis were performed with software FFT ACTRAN.
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Optimisation of electric vehicles gearbox housing with focus on emitted noise
Fürich, Adam ; Prokop, Aleš (referee) ; Řehák, Kamil (advisor)
This thesis deals with the vibration and noise of the gearbox for an electric vehicle. The gearbox itself acts as an emitter of unwanted noise caused by vibration excitation of the internal components of the gearbox. It is therefore necessary to deal with a complex unit such as the gearbox at system level as soon as it is being designed. This can be achieved using analytical tools and software. In this thesis, in order to evaluate the validity of the designed gearbox, a procedure is developed using both technical experiment and numerical simulations. The effect of the bolt preload is not overlooked for the following computational modelling dealing with the radiated noise of the gearbox. The results obtained from the performed technical experiments were then evaluated and compared with the results of the computational models. In this way, it is possible to compare the different approaches, their limitations and weaknesses. Furthermore, the thesis deals with design modifications that are analyzed using computational modeling. The thesis is concluded with a brief description of the application of each approach and an evaluation of the gearbox design modifications.
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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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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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Application for dysarthria examination using test 3F for Android
Sarker, Joy Tomáš ; Mekyska, Jiří (referee) ; Mucha, Ján (advisor)
This bachelor thesis focuses on diagnosing dysarthria thru a diagnosis apparatus called “Test 3F dysarthria profile“. During an examination with the apparatus the examined person undergoes 45 exercises that are meant to test respiration, phonation, phonetics, and the volubility of certain speech organs. The examiner, a clinical speech therapist, assesses the execution quality of each exercise with a number from 0 to 2. On the grounds of received points from all the exercises the level of dysarthria is diagnosed. The 3F test in this bachelor thesis is implemented as an Android application for Android devices and is supplemented by a partial automation of the examination based on an acoustic analysis of recorded speech of the examinee. The recorded speech is pre-processed by segmentation into 25 ms long frames using Hamming window. From this aforementioned speech recording we can determine speech fundamental frequency, jitter, and shimmer. The main goal and outcome is the creation of a modern mobile application for Android devices which, with the help of the 3F test, will make diagnosing dysarthria easier.
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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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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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