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.

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.

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.

The speech of individuals with Parkinson's disease (PD) is typically characterized as lacking in proper prosody because of its monopitch and monoloud quality, in addition to its reduced intensity. These qualities make it difficult for others to understand speakers with PD. The purpose of the current study was to identify what individuals with PD would do vocally, if anything at all, to improve speech production following a simulated misunderstanding of what they had just said. The study evaluated the performance of 5 individuals with PD and compared their performance to 5 age- and sex-matched controls. Specifically, measures of vocal intensity (loudness), fundamental frequency (pitch), and utterance duration were made for repetitions of a ‘misheard’ phrase. In one experimental condition noise was presented through headphones to induce the Lombard effect. Both individuals with PD and controls used increased duration as a means of enhancing clarity in a repetition. Fundamental frequency (F0) and sound pressure level (SPL) were not consistently modified in repetitions for clarification. Under most speaking conditions, individuals with PD and controls had similar F0 and SPL. Individuals with PD, like the controls, responded to the presentation of masking noise by increasing their fundamental frequency and their intensity. Therefore, not all individuals with PD exhibit difficulty using prosody.

This diploma thesis presents an analysis of speech impairments accompanying Parkinson's disease. This paper is divided into two sections - theoretical and practical. The first section gives a brief overview of available Czech and foreign literature and articles about this neurodegenerative disease. The section examines the questions of its aetiology, symptomatology, diagnostics and treatment. There can be found also a description of speech impairment connected with this disease, which is mainly hypokinetic and hyperkinetic dysarthria and dysphagia. It also deals with the problem of other limitations in communication such as facial bradykinesia or speech intelligibility. In the last chapter of theoretical part is described speech therapy and intervention with the emphasis on therapy, diagnostics and the effects of pharmacology an non-pharmacological treatment such as deep brain stimulation on speech performance in Parkinson's disease. The research part analyses speech impairment of people with Parkinson's disease. The aim of this diploma thesis was examination of this speech impairment using the Test 3F: Dysarthria profile and patient's own perception of this specific speech disorder. In this paper are presented eight case studies focusing on speech of clients with Parkinson's disease. The results...

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.