|
|
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.
|
|
|
The analysis of acoustic properties of innovative wood-based materials
Svojanovský, Jan ; Nop, Patrik (referee) ; Jirásek, Ondřej (advisor)
The content of this work is research of innovative wood-based materials in the acous-tic perspective. Basically, the goal is to determine the influence of the internal structure of the wood-based material on variables, which are sound speed, acoustic resistance, or acous-tic constant. In total were measured 15 samples, each one was made in a different way. Each sam-ple was weighted, and the length of its sides was measured. By using the ultra sonic timer, it was measured the time of the sound impulses, which go through the samples in all of their axes for the purpose of observation the influence the fibers on speed of sound in the materi-al. The next thing to do was to determine another magnitude, which describe acoustic of the material. All measurements were taken in room conditions (room humidity). Six of the sam-ples, which were randomly selected from those 15 samples, were measured once again after two weeks. In those two weeks were all the samples saved in air-conditioned place in which was the room humidity distinct lower. In the end all the samples were compared with each other and they were also com-pared with materials which are commonly used in production of musical instruments or acoustic components. Possible use in the music industry was assigned to each sample.
|
|
|
The analysis of acoustic properties of innovative wood-based materials
Svojanovský, Jan ; Nop, Patrik (referee) ; Jirásek, Ondřej (advisor)
The content of this work is research of innovative wood-based materials in the acous-tic perspective. Basically, the goal is to determine the influence of the internal structure of the wood-based material on variables, which are sound speed, acoustic resistance, or acous-tic constant. In total were measured 15 samples, each one was made in a different way. Each sam-ple was weighted, and the length of its sides was measured. By using the ultra sonic timer, it was measured the time of the sound impulses, which go through the samples in all of their axes for the purpose of observation the influence the fibers on speed of sound in the materi-al. The next thing to do was to determine another magnitude, which describe acoustic of the material. All measurements were taken in room conditions (room humidity). Six of the sam-ples, which were randomly selected from those 15 samples, were measured once again after two weeks. In those two weeks were all the samples saved in air-conditioned place in which was the room humidity distinct lower. In the end all the samples were compared with each other and they were also com-pared with materials which are commonly used in production of musical instruments or acoustic components. Possible use in the music industry was assigned to each sample.
|
guest ::
