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Automated segmentation of the diadochokinetic task for remote monitoring of hypokinetic dysarthria
Svojanovský, Jan ; Mekyska, Jiří (referee) ; Kováč, Daniel (advisor)
The study describes health problems associated with Parkinson’s disease, especially hypokinetic dysarthria. It also points out the subjective and objective methods used to determine the severity of the disease. One of these methods is a diadochokinetic (DDK) task based on rapid syllable repetition to test the functionality of the articulatory apparatus (e.g., tongue, lips, or vocal cords). Correct speech production can also be examined by a speech therapist in the 3F test, which scores the severity of disorders in different areas of speech production. Next, the approaches of other authors, also dealing with the automated search of syllables in the speech signal, are described. The thesis also discusses some features of human speech that are needed for training a machine learning model. These features were computed for each of the 30 ms segments of a DDK task. The main goal is the automated detection and classification of [Pa]-[Ta]-[Ka] syllables in the recordings. For this purpose, an algorithm using a logistic regression was applied. The resulting average accuracy of syllable detection in the recordings was 89.4 %, average sensitivity 59.0 % and average specificity 93.79 %. The identification of individual syllable types was successful with an average accuracy of 90.78 %, an average sensitivity of 59.0 % and an average specificity of 95.39 %. Considering that the predicted onset was not located directly on the manually annotated onset, but in its close vicinity (up to ±3 segments), the average detection sensitivity and average syllable type classification sensitivity were 96.9 % and 85.1 % respectively, with an average difference between manually annotated and automatically segmented syllable onsets of 10.35 ms. The average accuracy of classification of speakers into healthy and PN patients using logistic regression (with speech parameters obtained after automated segmentation) was only 43.92 %, sensitivity 70.0 % and specificity 30.61 % (threshold 70 %). Using linear regression, the clinical scores of the 3F test were predicted. For faciokinesis, the root mean square error (RMSE) was 2.764 after manual syllable annotation and 3.271 after automated segmentation. The RMSE values for phonetics were 3.657 (manual) and 0.753 (automated). The developed algorithm can detect syllables in DDK tasks with relative success, and thus it is possible to determine parameters quantifying speech disorders with low differences with manual segmentation. If the recordings of DDK tasks meet the conditions for computing all these parameters, the algorithm could be used to classify speakers into healthy subjects and PN patients, for whom it could additionally assess the severity of dysarthria.
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Monitoring of long-term effects of repetitive transcranial magnetic stimulation on speech and voice in patients with Parkinson's disease
Kaplan, Václav ; Novotný, Kryštof (referee) ; Mekyska, Jiří (advisor)
An individual's response to dopaminergic therapy for Parkinson's Disease (PD), which often presents with hypokinetic dysarthria, varies in its effect on speech disorders. This study examines the long-term effects of alternative treatment using repetitive transcranial magnetic stimulation (rTMS) in PD patients. The aim is to conduct research into acoustic and statistical analyses employed in similar studies in the past, quantify treatment-induced changes using a set of parameters, and statistically evaluate the outcomes. Acoustic parameters describing phonation, articulation, and prosody (areas of speech production) are selected. A database of recordings from patients with mild PD is utilized, from which 18 patients are chosen, participating in one pre-treatment measurement and four post-treatment measurements. Patients are divided into active and sham (placebo) groups. Observable changes in several parameters, particularly in phonation, are noted after rTMS treatment. However, the statistical analysis of acoustic parameters also highlights a significant placebo effect, as good, and often comparable results are observed in the sham group as well.
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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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State of the art speech features used during the Parkinson disease diagnosis
Bílý, Ondřej ; Smékal, Zdeněk (referee) ; Mekyska, Jiří (advisor)
This work deals with the diagnosis of Parkinson's disease by analyzing the speech signal. At the beginning of this work there is described speech signal production. The following is a description of the speech signal analysis, its preparation and subsequent feature extraction. 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, VOT, etc.). Another part of the work deals with the selection and reduction symptoms using the learning algorithms (SVM, ANN, k-NN) and their subsequent evaluation. In the last part of the thesis is described a program to count symptoms. Further is described selection and the end evaluated all the result.
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Degree of Parkinson's disease estimation based on acoustic analysis of speech
Ustohalová, Iveta ; Kiska, Tomáš (referee) ; Galáž, Zoltán (advisor)
The diploma thesis deals with the non-invasive analysis of progression of Parkinson´s disease using the acoustic analysis of speach. Hypokinetic dysarthria in connection with Parkinson´s disease as well as speech parameters are described in this work. Speech parameters are sorted according to the speech component they affect. The work uses the phonation of vowels "a" speech task as the most commonly used speech task in the field of pathological speech processing, because of its resistance to demographic and linguistic characteristics of the speakers. Based on obtained knowledge, in MATLAB development enviroment were created systém for UPDRS III scale estimation. The UPDRS III scale is based on subjective diagnosis given by the doctor. At first, one individual parameter is used for the UPDRS III scale value estimation. Then the feature selection using SFFS algorithm is applied to gain feature combination with minimal estimation errror. Attention i salso paid to correlation between individual symptoms and UPDSR III scale.
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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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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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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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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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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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