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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 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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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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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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