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Wyświetlanie 1-4 z 4
Tytuł:
Automatic disordered sound repetition recognition in continuous speech using CWT and kohonen network
Autorzy:
Codello, I.
Kuniszyk-Jóźkowiak, W.
Smołka, E.
Kobus, A.
Powiązania:
https://bibliotekanauki.pl/articles/106192.pdf
Data publikacji:
2012
Wydawca:
Uniwersytet Marii Curie-Skłodowskiej. Wydawnictwo Uniwersytetu Marii Curie-Skłodowskiej
Tematy:
speech recognition
speech disorders
sound repetition
Continuous Wavelet Transform
WaveBlaster
Opis:
Automatic disorders recognition in speech can be very helpful for a therapist while monitoring therapy progress of patients with disordered speech. This article is focused on sound repetitions. The signal is analyzed using Continuous Wavelet Transform with 16 bark scales. Using the silence finding algorithm, only speech fragments are automatically found and cut. Each cut fragment is converted into a fixed-length vector and passed into the Kohonen network. Finally, the Kohonen winning neuron result is put on the 3-layer perceptron. Most of the analysis was performer and the results were obtained using the authors’ program WaveBlaster. We use the STATISTICA package for finding the best perceptron which was then imported back into WaveBlaster and used for automatic blockades finding. The problem presented in this article is a part of our research work aimed at creating an automatic disordered speech recognition system.
Źródło:
Annales Universitatis Mariae Curie-Skłodowska. Sectio AI, Informatica; 2012, 12, 2; 39-48
1732-1360
2083-3628
Pojawia się w:
Annales Universitatis Mariae Curie-Skłodowska. Sectio AI, Informatica
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Automatic prolongation recognition in disordered speech using CWT and Kohonen network
Autorzy:
Codello, I.
Kuniszyk-Jóźkowiak, W.
Smołka, E.
Kobus, A.
Powiązania:
https://bibliotekanauki.pl/articles/332965.pdf
Data publikacji:
2012
Wydawca:
Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:
sieć Kohonena
zaburzenia automatycznego rozpoznawania mowy
ciągła transformata falkowa
skala Barka
wydłużenie mowy
Kohonen network
automatic disorders speech recognition
waveblaster
CWT
continuous wavelet transform (CWT)
Bark scale
speech prolongations
Opis:
Automatic disorder recognition in speech can be very helpful for the therapist while monitoring therapy progress of the patients with disordered speech. In this article we focus on prolongations. We analyze the signal using Continuous Wavelet Transform with 18 bark scales, we divide the result into vectors (using windowing) and then we pass such vectors into Kohonen network. Quite large search analysis was performed (5 variables were checked) during which, recognition above 90% was achieved. All the analysis was performed and the results were obtained using the authors' program - "WaveBlaster". It is very important that the recognition ratio above 90% was obtained by a fully automatic algorithm (without a teacher) from the continuous speech. The presented problem is part of our research aimed at creating an automatic prolongation recognition system.
Źródło:
Journal of Medical Informatics & Technologies; 2012, 20; 137-144
1642-6037
Pojawia się w:
Journal of Medical Informatics & Technologies
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Disordered sound repetition recognition in continuous speech using CWT and Kohonen network
Autorzy:
Codello, I.
Kuniszyk-Jóźkowiak, W.
Smołka, E.
Kobus, A.
Powiązania:
https://bibliotekanauki.pl/articles/333359.pdf
Data publikacji:
2011
Wydawca:
Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:
sieć Kohonena
zaburzenia automatycznego rozpoznawania mowy
ciągła transformata falkowa
skala Barka
powtarzanie dźwięku
Kohonen network
automatic disorders speech recognition
waveblaster
CWT
continuous wavelet transform (CWT)
Bark scale
sound repetition
Opis:
Automatic disorders recognition in speech can be very helpful for therapist while monitoring therapy progress of patients with disordered speech. This article is focused on sound repetitions. The signal is analyzed using Continuous Wavelet Transform with 16 bark scales, the result is divided into vectors and passed into Kohonen network. Finally, the Kohonen winning neuron result is put on the 3-layer perceptron. The recognition ratio was increased by about 20% by adding a modification into the Kohonen network training process as well as into CWT computation algorithm. All the analysis was performed and the results were obtained using the authors' program ”WaveBlaster“, The problem presented in this article is a part of our research work aimed at creating an automatic disordered speech recognition system.
Źródło:
Journal of Medical Informatics & Technologies; 2011, 17; 123-130
1642-6037
Pojawia się w:
Journal of Medical Informatics & Technologies
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Time–frequency Analysis of the EMG Digital Signals
Autorzy:
Kuniszyk-Jóźkowiak, W.
Jaszczuk, J.
Sacewicz, T.
Codello, I.
Powiązania:
https://bibliotekanauki.pl/articles/106250.pdf
Data publikacji:
2012
Wydawca:
Uniwersytet Marii Curie-Skłodowskiej. Wydawnictwo Uniwersytetu Marii Curie-Skłodowskiej
Tematy:
time-frequency analysis
EMG signal
Fast Fourier Transform
predictive analytics
wavelet analysis
Opis:
In the article comparison of time-frequency spectra of EMG signals obtained by the following methods: Fast Fourier Transform, predictive analysis and wavelet analysis is presented. The EMG spectra of biceps and triceps while an adult man was flexing his arm were analysed. The advantages of the predictive analysis were shown as far as averaging of the spectra and determining the main maxima are concerned. The Continuous Wavelet Transform method was applied, which allows for the proper distribution of the scales, aiming at an accurate analysis and localisation of frequency maxima as well as the identification of impulses which are characteristic of such signals (bursts) in the scale of time. The modified Morlet wavelet was suggested as the mother wavelet. The wavelet analysis allows for the examination of the changes in the frequency spectrum in particular stages of the muscle contraction. Predictive analysis may also be very useful while smoothing and averaging the EMG signal spectrum in time.
Źródło:
Annales Universitatis Mariae Curie-Skłodowska. Sectio AI, Informatica; 2012, 12, 2; 20-25
1732-1360
2083-3628
Pojawia się w:
Annales Universitatis Mariae Curie-Skłodowska. Sectio AI, Informatica
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-4 z 4

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