Analysis of Speech Signals for the Purpose of Neurological Disorders IT Diagnosis

Tato práce se zabývá návrhem systému analýzy hypokinetické dysartrie, jakožto poruchy motorické realizace řeči, která se vyskytuje u přibližně 90 % pacientů s Parkinsonovou nemocí. Pozornost je zde věnována především parametrizačním technikám, pomocí kterých je možné toto onemocnění diagnostikovat, monitorovat a odhadnout jeho progresi. Dále jsou v práci nalezeny řečové parametry, které nejvíce korelují se subjektivními testy, a pomocí kterých je možné odhadnout hodnoty různých hodnotících škál, jako např. unifikované škály pro hodnocení Parkinsonovy nemoci (UPDRS), či testu kognitivních funkcí (MMSE). V práci je rovněž navržen protokol akvizice dysartrické řeči, který lze v kombinaci s akustickou analýzou použít k odhadu zatížení hypokinetickou dysartrií v oblasti faciokineze, fonorespirace a fonetiky (korelace s 3F testem). Z hlediska parametrizace jsou pak v práci uvedeny zcela nové parametry založené na modulačním spektru, sluchové struktuře, bikepstru, aproximační a vzorkové entropii, empirické modální dekompozici a singulárních bodech. Všechny navržené techniky jsou integrovány do uceleného konceptu systému tak, že je možné jej implementovat v nemocnici a používat k výzkumu či hodnocení tohoto onemocnění.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.

Identification of persons via voice imprint

Tato práce se zabývá textově závislým rozpoznáváním řečníků v systémech, kde existuje pouze omezené množství trénovacích vzorků. Pro účel rozpoznávání je navržen otisk hlasu založený na různých příznacích (např. MFCC, PLP, ACW atd.). Na začátku práce je zmíněn způsob vytváření řečového signálu. Některé charakteristiky řeči, důležité pro rozpoznávání řečníků, jsou rovněž zmíněny. Další část práce se zabývá analýzou řečového signálu. Je zde zmíněno předzpracování a také metody extrakce příznaků. Následující část popisuje proces rozpoznávání řečníků a zmiňuje způsoby ohodnocení používaných metod: identifikace a verifikace řečníků. Poslední teoreticky založená část práce se zabývá klasifikátory vhodnými pro textově závislé rozpoznávání. Jsou zmíněny klasifikátory založené na zlomkových vzdálenostech, dynamickém borcení časové osy, vyrovnávání rozptylu a vektorové kvantizaci. Tato práce pokračuje návrhem a realizací systému, který hodnotí všechny zmíněné klasifikátory pro otisk hlasu založený na různých příznacích.This work deals with the text-dependent speaker recognition in systems, where just a few training samples exist. For the purpose of this recognition, the voice imprint based on different features (e.g. MFCC, PLP, ACW etc.) is proposed. At the beginning, there is described the way, how the speech signal is produced. Some speech characteristics important for speaker recognition are also mentioned. The next part of work deals with the speech signal analysis. There is mentioned the preprocessing and also the feature extraction methods. The following part describes the process of speaker recognition and mentions the evaluation of the used methods: speaker identification and verification. Last theoretically based part of work deals with the classifiers which are suitable for the text-dependent recognition. The classifiers based on fractional distances, dynamic time warping, dispersion matching and vector quantization are mentioned. This work continues by design and realization of system, which evaluates all described classifiers for voice imprint based on different features.

Linear prediciton and cepstral synthesis of speech signal in the TTS system

Práce se zabývá lineární predikční a kepstrální syntézou řečového signálu v systémech TTS (Text-to-Speech) s možností modelování prozodie. Je zde uveden popis řečového signálu v akustické a fonetické rovině, princip tvorby řeči a způsob znázornění řečového signálu v časové a kmitočtové oblasti. Dále je zde uvedena bloková stavba TTS systémů, přičemž každý blok je zvlášť detailně popsán. V práci je také popsána problematika modelování prozodie pomocí tří nejdůležitějších suprasegmentálních rysů (základní tón, trvání a intenzita řeči). Na konci je proveden návrh a realizace univerzálního českého TTS systému, který je založen na syntéze řeči v kmitočtové oblasti. Tento systém je implementován v programu MATLAB.This work deals with a linear prediction and cepstral synthesis of speech signal in the TTS (Text-to-Speech) systems with the opportunity of modeling the prosody. The work contains a description of speech signal in acoustic and phonetic plane, the principle of speech production and the way we can figure the speech signal in time and frequency domain. Next, there is the TTS block structure mentioned, whereas each block has its own detailed description. In the work, the modeling of prosody using the three most important suprasegmental features (fundamental tone, continuation and speech intensity) is also described. At the end of this work, there is a design and realization of universal Czech TTS system which is based on the speech synthesis in frequency domain. This system is implemented in program MATLAB.

Shannon entropy: A novel parameter for quantifying pentagon copying performance in non-demented Parkinson's disease patients

Introduction: Impaired copy of intersecting pentagons from the Mini-Mental State Examination (MMSE), has been used to assess dementia in Parkinson's disease (PD). We used a digitizing tablet during the pentagon copying test (PCT) as a potential tool for evaluating early cognitive deficits in PD without major cognitive impairment. We also aimed to uncover the neural correlates of the identified parameters using whole-brain magnetic resonance imaging (MRI). Methods: We enrolled 27 patients with PD without major cognitive impairment and 25 age-matched healthy controls (HC). We focused on drawing parameters using a digitizing tablet. Parameters with between-group differences were correlated with cognitive outcomes and were used as covariates in the whole-brain voxel-wise analysis using voxel-based morphometry; familywise error (FWE) threshold p < 0.001. Results: PD patients differed from HC in attention domain z-scores (p < 0.0001). In terms of tablet parameters, the groups differed in Shannon entropy (horizontal in-air, p = 0.003), which quantifies the movements between two strokes. In PD, a correlation was found between the median of Shannon entropy (horizontal in-air) and attention z-scores (R = -0.55, p = 0.006). The VBM revealed an association between our drawing parameter of interest and gray matter (GM) volume variability in the right superior parietal lobe (SPL). Conclusion: Using a digitizing tablet during the PCT, we identified a novel entropy-based parameter that differed between the nondemented PD and HC groups. This in-air parameter correlated with the level of attention and was linked to GM volume variability of the region engaged in spatial attention

Developmental Dysgraphia: A New Approach to Diagnosis

Writing is a complex skill. Issues in this process, which are usually associated with developmental dysgraphia (DD), could consistently cause problems in everyday life, like for example, lower self-esteem and poorer academic achievement. That is why the correct diagnosis of DD is crucial for further child development. DD belongs to the category of specific learning disabilities and according to different studies, its prevalence ranges between 0.1 and 30 percent. Diagnosing a child with DD relies, in the first place, on teachers. After that, psychologists, or special educational specialists (in the Czech Republic) commonly use qualitative evaluation of the written process, where the child is observed when he or she is writing. Nevertheless, there are no objective tests or standardized examinations for the assessment of handwriting deficiency either in special educational or psychological practices. In the frame of current research, a new quantitative approach to handwriting proficiency assessment was developed. Digitizing tablets (Wacom Intuos Pro L) with a special inking pen (Wacom Ink Pen) are used to record the online handwriting process and graphomotor skills of children. Administration templates contain simple graphomotor elements and complex figures related to DD symptoms and cognitive (memory and visuospatial) abilities. This new approach to diagnose handwriting issues will be presented in this article

Shannon entropy: A novel parameter for quantifying pentagon copying performance in non-demented Parkinson's disease patients

Introduction Impaired copy of intersecting pentagons from the Mini-Mental State Examination (MMSE), has been used to assess dementia in Parkinson's disease (PD). We used a digitizing tablet during the pentagon copying test (PCT) as a potential tool for evaluating early cognitive deficits in PD without major cognitive impairment. We also aimed to uncover the neural correlates of the identified parameters using whole-brain magnetic resonance imaging (MRI). Methods We enrolled 27 patients with PD without major cognitive impairment and 25 age-matched healthy controls (HC). We focused on drawing parameters using a digitizing tablet. Parameters with between-group differences were correlated with cognitive outcomes and were used as covariates in the whole-brain voxel-wise analysis using voxel-based morphometry; familywise error (FWE) threshold p < 0.001. Results PD patients differed from HC in attention domain z-scores (p < 0.0001). In terms of tablet parameters, the groups differed in Shannon entropy (horizontal in-air, p = 0.003), which quantifies the movements between two strokes. In PD, a correlation was found between the median of Shannon entropy (horizontal in-air) and attention z-scores (R = 0.55, p = 0.006). The VBM revealed an association between our drawing parameter of interest and gray matter (GM) volume variability in the right superior parietal lobe (SPL). Conclusion Using a digitizing tablet during the PCT, we identified a novel entropy-based parameter that differed between the nondemented PD and HC groups. This in-air parameter correlated with the level of attention and was linked to GM volume variability of the region engaged in spatial attention

Exploiting spectral and cepstral handwriting features on diagnosing Parkinson’s disease

Parkinson’s disease (PD) is the second most frequent neurodegenerative disease associated with several motor symptoms, including alterations in handwriting, also known as PD dysgraphia. Several computerized decision support systems for PD dysgraphia have been proposed, however, the associated challenges require new approaches for more accurate diagnosis. Therefore, this work adds spectral and cepstral handwriting features to the already-used temporal, kinematic and statistics handwriting features. First, we calculate temporal and kinematic features using displacement; statistic features (SF) using displacement, and horizontal and vertical displacement; spectral (SDF) and cepstral (CDF) using displacement, horizontal and vertical displacement and pressure. Since the employed dataset (PaHaW) contains only 37 PD patients and 38 healthy control subjects (HC), then as the second step, we augment the percentage of the smaller training set to equal the larger. Next, we augment both classes to increase the training patient’s data and added random Gaussian noise in all augmentations. Third, the most relevant features were selected using the modified fast correlation-based filtering method (mFCBF). Finally, autoML is employed to train and test more than ten plain and ensembled classifiers. Experimental results show that adding spectral and cepstral features to temporal, kinematics and statistics features highly improved classification accuracy to 98.57%. Our proposed model, with lower computational complexities, outperforms conventional state-of-the-art models for all tasks, which is 97.62%