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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 speech features quantifying diadochokinetic (DDK) tasks
Kukučka, Peter ; Smékal, Zdeněk (referee) ; Mekyska, Jiří (advisor)
Speech processing methods were studied to calculate parameters of pacient with Parkinon's disease. Main focus of this work is to examine diadochokinetic (DDK) tests. Algorithm for parameters extraction was proposed. It works in more parts. DC is removed from speech signal, preemphasis aplicated. Envelope of input signal is calculated, peaks of syllables are detected. Parameters and statistical results of Mann-Whitney U~test are calculated from detected peaks. Proposed algorithm is implemented in Matlab.
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Analysis of Speech Signals for the Purpose of Neurological Disorders IT Diagnosis
Mekyska, Jiří ; Dostál, Otto (referee) ; Přibilová, Anna (referee) ; Smékal, Zdeněk (advisor)
This work deals with a design of hypokinetic dysarthria analysis system. Hypokinetic dysarthria is a speech motor dysfunction that is present in approx. 90 % of patients with Parkinson’s disease. The work is mainly focused on parameterization techniques that can be used to diagnose or monitor this disease as well as estimate its progress. Next, features that significantly correlate with subjective tests are found. These features can be used to estimate scores of different scales like Unified Parkinson’s Disease Rating Scale (UPDRS) or Mini–Mental State Examination (MMSE). A protocol of dysarthric speech acquisition is introduced in this work too. In combination with acoustic analysis it can be used to estimate a grade of hypokinetic dysarthria in fields of faciokinesis, phonorespiration and phonetics (correlation with 3F test). Regarding the parameterization, features based on modulation spectrum, inferior colliculus coefficients, bicepstrum, approximate and sample entropy, empirical mode decomposition and singular points are originally introduced in this work. All the designed techniques are integrated into the system concept in way that it can be implemented in a hospital and used for a research on Parkinson’s disease or its evaluation.
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Correlation analysis of Parkinson's disease in the acoustic field
Vošček, Jakub ; Mekyska, Jiří (referee) ; Smékal, Zdeněk (advisor)
The topic of this bachelor thesis is the correlation analysis of Parkinson’s disease in the acoustic field. The first part is about the Parkinson disease and its symptoms. It looks closer on problems with speech production, which is called hypokinetic disarthria, and describes the causes of the problems as well as the kind of treatment that is used. The next part involves a study of the pre–processing of signal, i.e. removing the direct component, a preemphasis and a segmentation to smaller frames. Afterwards, individual parameters are calculated in the next step. It is also necessary to calculate simple statistics, for example a median, a standard deviation, etc. after the calculation of some parameters. The calculation of Pearson’s and Spearman’s correlation coefficients is included. Moreover, a block diagram for the data processing is suggested, which involves a description of the functions of the individual blocks. The program is explained in the practical part, which also features parts of the tables with parameters' values and the calculated coefficients. As the conclusion of the work, there are graphs which display correlations of the parameters and the paraclinical data.
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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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Acoustic analysis of sentences complicated for articulation in patients with Parkinson's disease
Kiska, Tomáš ; Smékal, Zdeněk (referee) ; Mekyska, Jiří (advisor)
This work deals with a design of hypokinetic dysarthria analysis system. Hypokinetic dysarthria is a speech motor dysfunction that is present in approx. 90 % of patients with Parkinson’s disease. Next there is described Parkinson's disease and change of the speech signal by this disability. The following describes the symptoms, which are used for the diagnosis of Parkinson's disease (FCR, VSA, VAI, etc.). The work is mainly focused on parameterization techniques that can be used to diagnose or monitor this disease as well as estimate its progress. A protocol of dysarthric speech acquisition is described in this work too. In combination with acoustic analysis it can be used to estimate a grade of hypokinetic dysarthria in fields of faciokinesis, phonorespiration and phonetics (correlation with 3F test). Regarding the parameterization, new features based on method RASTA. The analysis is based on parametrization sentences complicated for articulation. Experimental dataset consists of 101 PD patients with different disease progress and 53 healthy controls. For classification with feature selection have selected method mRMR.
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Application for the calculation of speech features describing hypokinetic dysarthria
Hynšt, Miroslav ; Mekyska, Jiří (referee) ; Kiska, Tomáš (advisor)
This thesis is about design and implementation of application for computing speech parameters on people with Parkinson disease. At the beginning is generaly described Parkinson disease and Hypokinetic dysarthria and how it affects the speech and speech parameters when it occurs. Mainly there are described areas of speech like phonation, prosody, articulation and fluent speech. As a part of next topic this thesis describes specific speech parameters with bigger meaning during diagnosis Parkinson disease and it's progress over the time. There are also mentioned few significant studies dealing with examination of speech of the subjects with diagnoses of Parkinson disease and computing some speech parameters in order to analyze their speech impairments. Part of the thesis is description of implemented standalone application for calculating, exporting and visualizing of speech parameters from selected sound records.
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Analysis of Parkinson's disease using segmental speech parameters
Mračko, Peter ; Mekyska, Jiří (referee) ; Smékal, Zdeněk (advisor)
This project describes design of the system for diagnosis Parkinson’s disease based on speech. Parkinson’s disease is a neurodegenerative disorder of the central nervous system. One of the symptoms of this disease is disability of motor aspects of speech, called hypokinetic dysarthria. Design of the system in this work is based on the best known segmental features such as coefficients LPC, PLP, MFCC, LPCC but also less known such as CMS, ACW and MSC. From speech records of patients affected by Parkinson’s disease and also healthy controls are calculated these coefficients, further is performed a selection process and subsequent classification. The best result, which was obtained in this project reached classification accuracy 77,19%, sensitivity 74,69% and specificity 78,95%.
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Application of statistical analysis of speech in patients with Parkinson's disease
Bijota, Jan ; Mžourek, Zdeněk (referee) ; Galáž, Zoltán (advisor)
This thesis deals with speech analysis of people who suffer from Parkinson’s disease. Purpose of this thesis is to obtain statistical sample of speech parameters which helps to determine if examined person is suffering from Parkinson’s disease. Statistical sample is based on hypokinetic dysarthria detection. For speech signal pre-processing DC-offset removal and pre-emphasis are used. The next step is to divide signal into frames. Phonation parameters, MFCC and PLP coefficients are used for characterization of framed speech signal. After parametrization the speech signal can be analyzed by statistical methods. For statistical analysis in this thesis Spearman’s and Pearson’s correlation coefficients, mutual information, Mann-Whitney U test and Student’s t-test are used. The thesis results are the groups of speech parameters for individual long czech vowels which are the best indicator of the difference between healthy person and patient suffering from Parkinson’s disease. These result can be helpful in medical diagnosis of a patient.
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Django framework based web application for objective analysis of hypokinetic dysarthria
Čapek, Karel ; Zvončák, Vojtěch (referee) ; Galáž, Zoltán (advisor)
This master´s thesis deals with the calculation of parameters that would be able to differentiate healthy speech and speech impaired by hypokinetic dysarthria. There was staged hypokinetic dysarthria, which is a motoric disorder of speech and vocal tract. Were studied speech signal processing methods. Further parameters were studied, which could well differentiate healthy and diseased speech. Subsequently, these parameters were programmed in Python programming language. The next step was to create a web application in Django framework, which is used for the analysis of the dyzartic speech.
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