The aim of this thesis is to investigate computerized voice assessment methods to classify between the normal and Dysarthric speech signals. In this proposed system, computerized assessment methods equipped with signal processing and artificial intelligence techniques have been introduced. The sentences used for the measurement of inter-stress intervals (ISI) were read by each subject. These sentences were computed for comparisons between normal and impaired voice. Band pass filter has been used for the preprocessing of speech samples. Speech segmentation is performed using signal energy and spectral centroid to separate voiced and unvoiced areas in speech signal. Acoustic features are extracted from the LPC model and speech segments from each audio signal to find the anomalies. The speech features which have been assessed for classification are Energy Entropy, Zero crossing rate (ZCR), Spectral-Centroid, Mean Fundamental-Frequency (Meanf0), Jitter (RAP), Jitter (PPQ), and Shimmer (APQ). Naïve Bayes (NB) has been used for speech classification. For speech test-1 and test-2, 72% and 80% accuracies of classification between healthy and impaired speech samples have been achieved respectively using the NB. For speech test-3, 64% correct classification is achieved using the NB. The results direct the possibility of speech impairment classification in PD patients based on the clinical rating scale.

Parkinson's disease (PD) is a degenerative illness whose cardinal symptoms include rigidity, tremor, and slowness of movement. In addition to its widely recognized effects PD can have a profound effect on speech and voice.The speech symptoms most commonly demonstrated by patients with PD are reduced vocal loudness, monopitch, disruptions of voice quality, and abnormally fast rate of speech. This cluster of speech symptoms is often termed Hypokinetic Dysarthria.The disease can be difficult to diagnose accurately, especially in its early stages, due to this reason, automatic techniques based on Artificial Intelligence should increase the diagnosing accuracy and to help the doctors make better decisions. The aim of the thesis work is to predict the PD based on the audio files collected from various patients.Audio files are preprocessed in order to attain the features.The preprocessed data contains 23 attributes and 195 instances. On an average there are six voice recordings per person, By using data compression technique such as Discrete Cosine Transform (DCT) number of instances can be minimized, after data compression, attribute selection is done using several WEKA build in methods such as ChiSquared, GainRatio, Infogain after identifying the important attributes, we evaluate attributes one by one by using stepwise regression.Based on the selected attributes we process in WEKA by using cost sensitive classifier with various algorithms like MultiPass LVQ, Logistic Model Tree(LMT), K-Star.The classified results shows on an average 80%.By using this features 95% approximate classification of PD is acheived.This shows that using the audio dataset, PD could be predicted with a higher level of accuracy.

The general topic of this work is to reveal the potential relationship between tiredness cause by hypokinetic monotonous loading and breathing. The aim was to determine if there are suitable respiratory parameters that would indicate this tirednes and, if so, then verify their validity for predicting the tiredness phenomena accompanying huge range of everyday human activities. The performed experiment was attended by five volunteers who absolved measurements of electrical activity of brain, of breathing and of chest volume changes. The course of the experiment and the behaviour of probands were recorded by a camera. In the first part of each maesuremnt, a specified monotonous task (Task Tracking) was performed. The probands have had to follow the target moving with pseudocasual direction and speed by the cursor on the monitor. This task currently reflected the level of reliability and quality of the performed aktivity. In the second part of measurement, the probands had just to relax and watch a movie. Both parts were measured in two conditions - alert and tired (after 24 hours of sleep deprivation) proband. The data were compared with each other and evaluated. The measurements and the results showed that the rate of the tiredness can be fairly reliably assessed by monitoring of the volume...

This work deals with analysis of phonation in patients with Parkinson’s disease (PD). Approximately 90% of patients with Parkinson’s disease suffer from speech motor dysfunction called hypokinetic dysarthria. System for Parkinson’s disease analysis from speech signals is proposed and several types of features are examined. Czech Parkinson’s speech database called PARCZ is used for classification. This dataset consists of 84 PD patients and 49 healthy controls. Results are evaluated in two ways. Firstly, features are individually analysed by Spearman correlation, mutual information and Mann-Whitney U test. Classification is based on random forests along with leave-one-out validation. Secondly, SFFS algorithm is employed for feature selection in order to get the best classification result. Proposed system is tested for each gender individually and both genders together as well. Best result for both genders together is expressed by accuracy 89,47 %, sensitivity 91,67% and specificity 85,71 %. Results of this work showed that the most important vowel realizations for phonation analysis are sustained vowels pronounced with maximum or minimum intensity (not whispering).

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.

The general topic of this work is to reveal the potential relationship between tiredness cause by hypokinetic monotonous loading and breathing. The aim was to determine if there are suitable respiratory parameters that would indicate this tirednes and, if so, then verify their validity for predicting the tiredness phenomena accompanying huge range of everyday human activities. The performed experiment was attended by five volunteers who absolved measurements of electrical activity of brain, of breathing and of chest volume changes. The course of the experiment and the behaviour of probands were recorded by a camera. In the first part of each maesuremnt, a specified monotonous task (Task Tracking) was performed. The probands have had to follow the target moving with pseudocasual direction and speed by the cursor on the monitor. This task currently reflected the level of reliability and quality of the performed aktivity. In the second part of measurement, the probands had just to relax and watch a movie. Both parts were measured in two conditions - alert and tired (after 24 hours of sleep deprivation) proband. The data were compared with each other and evaluated. The measurements and the results showed that the rate of the tiredness can be fairly reliably assessed by monitoring of the volume...

OF THE THESIS This Master's thesis is dedicated to providing a complete account and analysis of activities designed to tackle specific logopaedic difficulties associated with Parkinson's disease. The principal theoretical sections of this thesis are concerned with an in-depth overview of Parkinson's disease and hypokinetic dysarthia (a speech disorder associated with Parkinson's disease), the dynamics of working with a group of senior participants, the Parkinson Society, the place of logopaedic therapy in Society and of specific components of logopaedic therapy aimed at persons with Parkinson's disease. The practical section which follows then proposes and carefully outlines specific activities suitable for such therapy. Activities are classified by their objectives - areas they intend to exercise improve. Designs of two model lessons comprising a combination of the activities proposed in the preceding chapters are included in the final section of the thesis.

One of the key features of the hypokinetic dysarthria associated with Parkinson disease is dysprosody. While there has been ample research into the global characterization of speech in Parkinson disease, little is known about how people with Parkinson disease mark lexical stress. This study aimed to determine how people with Parkinson disease modulate pitch, intensity, duration, and vowel space to differentiate between two common lexical stress patterns in English: trochees (strong-weak pattern) and iambs (weak-strong pattern), in two syllable words. Twelve participants with mild to moderate idiopathic Parkinson disease and twelve age- and sex-matched controls completed a series of speech tasks designed to elicit token words of interest in prosodically-relevant speech tasks (picture identification (in isolation and lists) and giving directions (spontaneous speech). Results revealed that people with Parkinson disease produced a higher overall pitch and a smaller vowel space as compared to controls, though most lexical marking features were not significantly different. Importantly, the elicitation task had a significant effect on most dependent measures. Although lexical stress is not significantly impacted by Parkinson disease, we recommend that future research and clinical practice focus more on the use of spontaneous speech tasks rather than isolated words or lists of words due to the differences in the marking of lexical stress in the latter tasks, making them less useful as ecologically-valid assessments of prosody in everyday communication.

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 %.

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.